Category: Artificial Intelligence

TeacherMatic in Kenya: Insights from a One-Day Pilot with Educators

Posted on by Miguel Rivera

How can AI tools support teachers while respecting local contexts, infrastructure limits and professional expertise? This piece examines a TeacherMatic pilot in Kenya, where secondary school teachers explored AI-powered generators. By reflecting on practical challenges such as connectivity and curriculum alignment, the article considers how responsibly designed AI can enhance learning and promote inclusive classroom innovation.

TeacherMatic in Kenya: Insights from a One-Day Pilot with Educators

Author: Carles Vidal, MSc in Digital Education, Business Director of Avallain Lab

Kenya, August 2025 – In May 2025, the Avallain Lab, in collaboration with the Avallain Foundation, conducted a one-day pilot with Kenyan teachers to explore how generative AI tools could support them in their daily educational work. The initiative focused on TeacherMatic, Avallain’s AI toolkit for teachers, aiming to gain early insights on its suitability for the Kenyan context and identify potential improvement areas.

From Research to Pilot Design

In January 2025, Teaching with GenAI: Insights on Productivity, Creativity, Quality and Safety, an independent, research-driven report commissioned by the Avallain Group and produced by Oriel Square Ltd, was published. It explores how GenAI can enhance teaching and learning while addressing educational opportunities, challenges and ethical considerations. Building on this, the pilot translated the report’s themes into a series of sessions featuring hands-on activities for teachers. These sessions allowed participants to discuss and apply the report’s ideas in practical activities and add new perspectives to the conversation.

With this purpose in mind, twelve local secondary school teachers, representing both public and private institutions, were selected to provide a sample consistent with the previous study. 

In preparation for the pilot, the Kenyan curriculum was incorporated into TeacherMatic’s curriculum alignment generation options so that the participants could use it to inform their content requests. Since a phased implementation of a new curriculum is currently underway in Kenya, both the existing and the upcoming versions were included to provide teachers with all possible options in this transition context.

The pilot was organised in three parts. It began with a focus group designed to capture participants’ initial impressions and existing knowledge of GenAI tools, while also introducing them to TeacherMatic. This was followed by breakout sessions, where smaller groups of teachers engaged in hands-on exploration of the tool. The day concluded with a plenary session, bringing everyone together to share insights and provide feedback.

Infrastructure Challenges

During the initial focus group, teachers described existing infrastructure challenges relating to both the availability of devices and the reliability of internet connections, as part of the general context of their teaching practices. Connectivity was identified as a critical barrier, with ‘slow or unreliable internet and, in some cases, complete service interruptions lasting hours’ being common in many public institutions. According to the group, while private schools tend to experience fewer connectivity issues, many public schools continue to face significant barriers due to their reliance on intermittent mobile networks. 

Participants also reported limited access to devices, particularly in public schools, where ‘only a few computers are available and shared among all teachers’. Most public schools operate under centralised device policies, with limited computer labs and few, if any, classroom-based devices. In this context, mobile phones become the primary means of accessing tools such as educational technologies.

Interactive Breakout SessionsIn the breakout sessions, teachers explored a curated set of TeacherMatic generators, including ‘Lesson Plan’, ‘Multiple Choice Questions’, ‘Debate’, ‘True or False’, ‘Learning Activities’ and ‘Inspiration!’. Participants accessed TeacherMatic on computer devices, tablets and mobile phones and worked in Swahili and English during discussions and content generation.

A small group of participants at the Kenyan TeacherMatic pilot collaborate during a breakout session, reviewing notes and using the toolkit on digital devices.
During a breakout session, participants explore TeacherMatic generators together on a mobile device.

During the sessions, the teachers engaged freely with the generators, exchanging ideas, debating approaches and sharing expectations and concerns. Participants expressed strong enthusiasm for the potential of using GenAI tools in their classrooms, viewing them as a way to enhance teaching resources and remain ahead of their students in adopting this technology.

After the hands-on sessions, participants reconvened for a larger group discussion to share how they perceived TeacherMatic and, more broadly, GenAI tools, including what aspects attracted them, what concerns they had and what support or training they would need for effective adoption.

Findings and Reflections

The final group discussion revealed a general agreement on the following areas:

  • Time-saving benefits: Participants valued the speed and quality of the generated content and identified significant reductions in classroom preparation time, which they felt would allow them to improve the delivery of their lessons. As one teacher said, ‘If we can save time on planning, we can spend more time on students.’
  • Curriculum alignment: Although both current Kenyan curricula were included in TeacherMatic, participants saw opportunities for even more detailed curriculum integration, highlighting the need for further content localisation down to the most detailed level of curriculum implementation.
  • Creativity and pedagogical innovation: Teachers expressed a strong need for multimodal learner-facing content, such as clips or visuals, to help explain complex topics, ‘like 3D geometry’. With learners already using AI creatively, some felt that text-based outputs alone were insufficient. As one participant explained, ‘You can’t teach about the inside of a pyramid with text.’ 
  • AI literacy training programs for teachers: Teachers also voiced the importance of receiving training in GenAI so that students do not outpace them in its use. As one teacher expressed, ‘Let’s take this AI to the classroom… show them that their teachers are also up-to-date.’
  • Reassurance that GenAI tools are not a replacement for teachers:  Participants stressed the importance of teachers retaining full agency in creating and delivering learning resources, especially when validating content intended for their students.
A facilitator stands at the front of the room as participants in the TeacherMatic Kenyan pilot engage in a group discussion, with laptops and notes on the table.
Teachers and facilitators discuss key findings from the TeacherMatic Kenyan pilot, highlighting opportunities and challenges in classroom use.

Early Insights and Broader Lessons

While this was only a one-day pilot with a small group of teachers, it offered valuable, early insights into both the opportunities and barriers to adopting GenAI in Kenyan classrooms. Some challenges, like limited devices and connectivity, may be more specific to the region and require systemic solutions, but others, such as the need for curriculum-aligned content and teacher training, echo what we have seen elsewhere.

A group photo of all participants in the Kenyan TeacherMatic pilot, standing together outdoors under trees.
Thank you to Martina Amoth (CEO, Avallain Foundation East Africa), Robert Ochiel (Avallain Lab Intern) and all the participants of the Kenyan TeacherMatic pilot for sharing their time, reflections and experiences.

These shared lessons show that even small-scale pilots can guide product development and spark ideas for making GenAI a meaningful, inclusive tool for educators, regardless of where they teach.

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_ 

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Revisit the Language Teaching Takeoff Webinar Series: Featured Highlights and Insights

Posted on by admin

While taking a short summer break, we wanted to pause and review the best moments and most important insights from our Language Teaching Takeoff Webinar Series. If you missed an episode or want to revisit the practical tips and tools demonstrated in the TeacherMatic Language Teaching Edition, this blog highlights key takeaways and illustrates how a purpose-built AI supports language educators and enhances classroom practice.

Revisit the Language Teaching Takeoff Webinar Series: Featured Highlights and Insights

London, August 2025 – The Language Teaching Takeoff Webinar Series offers a practical look at the TeacherMatic Language Teaching Edition, a toolkit designed specifically for language educators. It’s more than a generic AI solution: every generator is built around the realities of classroom teaching, with a focus on saving time, enhancing creativity, maintaining pedagogical standards and ensuring the ethical and safe adoption of AI in language education. 

This edition of TeacherMatic can generate comprehensive lesson plans, adapt texts and tasks, create original content and quizzes, provide personalised feedback and more, all tailored to different CEFR levels. Each 30-minute session focuses on integrating AI meaningfully and responsibly, providing ideas, activities and workflows that make a real difference to teaching and learning.

The series has attracted over 300 educators across four sessions, underscoring the strong interest in practical, teacher-focused AI solutions.

Meet the Hosts

Moderated by Giada Brisotto, Senior Marketing and Sales Operations Manager at Avallain, and led by Nik Peachey, award-winning educator, author and edtech consultant, each webinar combines deep expertise with actionable guidance. 

‘These generators aren’t just text tools. They’re designed with real classroom needs in mind. You input your goals, level and theme, and the results are ready to use or refine.’ – Nik Peachey, Director of Pedagogy, PeacheyPublications

Save Time While Planning Quality Lessons

The first webinar in the series, ‘Elevate Your Lesson Planning’, explored how purpose-built AI can transform how teachers design lessons. One of the main insights from the session was the critical balance between efficiency and academic rigour. Nik demonstrated how the Lesson Plan generator enables educators to produce fully structured, CEFR-aligned lesson plans in just a few minutes. 

Key benefits highlighted in the session included:

  • CEFR-aligned outputs to ensure lessons meet recognised language standards.
  • Adaptable and editable plans that reflect the needs of individual classes.
  • Support for professional autonomy, giving teachers control instead of imposing rigid templates.
  • Support for core pedagogical models, including Communicative Language Teaching (CLT), Task-Based Learning (TBL), Presentation Practice Production (PPP), Lexical Approach and Test-Teach-Test.

The session emphasised that the real value of AI in education lies in targeted, purposeful support, rather than blanket automation. Starting with focused applications like lesson planning allows educators to make small, practical changes that can significantly impact both teaching quality and learners’ experiences.

Deliver Personalised CEFR-Aligned Feedback

The second webinar, ‘From Rubrics to Results: How to Provide Impactful Feedback’, focused on how AI can help teachers provide meaningful, personalised feedback without adding to their workload. Nik demonstrated the Feedback generator, showing how educators can instantly create feedback tailored to each student while keeping them aligned with CEFR standards and institutional rubrics.

Key benefits highlighted in the session included:

  • CEFR-aligned feedback that can be tailored to specific subscales.
  • Feedback tailored to rubrics and assessment criteria, ensuring comments reflect your teaching context.
  • Balanced, constructive comments that highlight both strengths and areas for improvement.

During the session, it was stressed that AI works best when it enhances teacher expertise rather than replacing it. By streamlining the feedback process, educators can maintain high standards of personalisation and pedagogy, even with large groups of students.

Adapt and Analyse Content Across Levels

The third webinar, ‘Adapting Content for Effective CEFR-Aligned Language Teaching’, spotlighted how AI can empower teachers to adapt existing materials to diverse learner groups and levels. Nik introduced two powerful tools specifically designed with classroom realities in mind: the Adapt your content generator and the CEFR Level Checker.

Key benefits highlighted in the session included:

  • Effortlessly adapting content from one CEFR level to another while preserving the original theme and ensuring the result is pedagogically effective.
  • Immediate, precise CEFR analysis of texts, breaking down vocabulary and grammar complexity to help verify learner-appropriate materials.
  • Supporting teacher control through editable outputs that can be fine-tuned for specific class needs.

As Nik emphasised, ‘It’s not just about saving time. It’s about creating something that actually works for your learners faster’. The session showed how these AI generators translate the complexity of CEFR adaptation into practical, editable resources, enabling teachers to respond precisely to different learner needs without compromising pedagogical integrity.

Engage Students and Assess Progress Quickly

‘Generate, Engage and Assess: Create Custom Texts and Multiple Choice Quizzes’, demonstrated how TeacherMatic can support both content creation and assessment in language teaching. Participants saw how the Create a text and Multiple Choice Questions generators allow teachers to produce original CEFR-level texts and assess learner understanding instantly, without prompt engineering or technical complexity.

Highlights from the session included:

  • Generating original classroom-ready texts tailored by topic, CEFR level, grammar focus, text type, vocabulary and length.
  • Creating CEFR-aligned multiple-choice quizzes from any text to assess comprehension, vocabulary or grammar.
  • Adapting content across proficiency levels while preserving the theme and ensuring pedagogical usefulness.

In this session, participants learned how combining flexible content and quiz generators can streamline lesson preparation, enhance learner engagement and support accurate, timely assessment.

The Language Teaching Takeoff Webinar Series has illustrated how purpose-built AI can support language educators in practical, impactful ways. The TeacherMatic Language Teaching Edition allows teachers to leverage AI responsibly, ethically and safely, enhancing learning while maintaining pedagogical standards and putting educators in control of their classroom practice.

The series isn’t over yet.

What’s Next:

After a short summer break, the Language Teaching Takeoff Webinar Series returns. Join us for the next session:

Create Engaging Materials from YouTube Content and Build Custom Glossaries

Date: Thursday, 11th September

Time: 12:00 – 12:30 BST | 13:00 – 13:30 CEST

Secure your place

Discover how AI generators can turn YouTube videos into engaging content, and learn how to generate custom glossaries tailored to CEFR levels and your learners’ needs.

Explore the Language Teaching Edition of TeacherMatic

Whether teaching A1 learners or guiding advanced students through C1 material, the Language Teaching Edition of TeacherMatic helps you do it more efficiently, precisely and flexibly. 

Explore it here

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_ 

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Bringing Mobile Learning Back with AI, Context and Expertise

Posted on by admin

What if mobile learning had the intelligence and context it lacked 25 years ago? This piece revisits the rise and fall of early mobile learning projects and considers how the convergence of artificial intelligence, contextual mobile data and educational expertise could support more responsive and personalised learning today.

Bringing Mobile Learning Back with AI, Context and Expertise

Author: Prof John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

St. Gallen, July 28, 2025 – Around 25 years ago, many members of the European edtech research community, myself included, were engaged in projects, pilots and prototypes exploring what was then known as ‘mobile learning’. This roughly and obviously referred to learning with mobile phones, likely 3G, nearing the dawn of the smartphone era. Learners could already access all types of learning available on networked desktops in their colleges and universities, but they were now freed from their desktops. The excitement, however, was around all the additional possibilities. 

One of these was ‘contextual learning,’ meaning learning that responded to the learner’s context. Mobile phones knew where they were, where they had been and what they had been doing1. These devices could capture images, video and sound of their context, including both the user and their surroundings. This meant they could also understand and know their user, the learner. 

So, to provide some examples:

  • Walking around art galleries like the Uffizi and heritage sites like Nottingham Castle, learners with their mobile phones could stop at a painting randomly and receive a range of background information, including audio, video and images. The longer they stayed, the more they would receive. Based on other paintings they had lingered at, they could get suggestions, explanations and perspectives on what else they might like and where else they could go.
  • Augmented reality on mobile phones meant that learners standing in Berlin using their mobile phone as a camera viewfinder could see the Brandenburg Gate, but with the now-gone Berlin Wall interposed perfectly realistically as they walked up to and around it. Similarly, they could see Rembrandt’s house in Amsterdam. Learners could also walk across the English Lake District and see bygone landforms and glaciers, or engage in London murder mysteries, looking at evidence and hearing witnesses at various locations.
  • Recommender systems on mobile phones analysed learners’ behaviours, achievements and locations to suggest the learning activity that would suit them best based on their history and context. These recommendations could be linked to assignments, resources and colleagues on their university LMS, providing guidance and practical advice. For example, in a Canadian project, there are specific applications in tourism.
  • Using a system like Molly Oxford on their mobile phones, learners could be guided to the nearest available loan copy of a library book they wanted. They could also be given suggestions based on public transport, wheelchair accessible footpaths and library opening hours.
  • Trainee professionals, such as physiotherapists or veterinary nurses, in various projects across Yorkshire, could be assessed while carrying out a healthcare procedure in ‘real-life’ practice. Their mobile phones would capture the necessary validation and contextual data to ensure a trustworthy process.
  • Some early experiments, with Bluetooth and other forms of NFC (near-field communication), allowed passers-by or students to pick up comments or images hanging in discrete locations, such as a subway or corridor on a university campus, serving as sign-posting or street art. 

These pilots and projects implemented situated2, authentic3 and personalised4 learning as aspects of contextual learning, and espoused5 the principles of constructivism6 and social constructivism7. This was only possible as far as the contemporary resources and technologies permitted. They did not, however, encourage or allow content to be created, commented on, or contributed to by learners, only consumed by them. Also, they usually only engaged with learners on an individual basis, not supporting interaction or communication among learners, even those learning the same thing, at the same place and at the same time.

So what went wrong? Why aren’t such systems widespread across communities, galleries, cultural spaces, universities and colleges any more? And how have things changed? Could we do better now?

The Downfall of Mobile Learning: What Went Wrong?

Mobile phone ownership was not widespread two decades ago, and popular mobile phones were not as powerful as they are today. The ‘apps economy’8 had not taken off. This meant that projects and pilots had to develop all software systems from scratch and get them to interoperate9. They also had to fund and provide the necessary mobile phones for the few learners involved10. 

Once the pilot or project and its funding had finished, its ideas and implementation were not scalable or sustainable; they were unaffordable. Pilots and projects were usually conducted within formal educational institutions among their students. Also, evaluation and dissemination focused on technical feasibility, proof-of-concept and theoretical findings. They rarely addressed outcomes that would sway institutional managers and impact institutional performance metrics. As a result, these ideas remained optional margins of institutional activity rather than the regulated business of courses, qualifications, assessments and certificates. Nor was there a business model to support long-term adoption. 

In fairness, we should also factor in the political and economic climate at the end of the 2000s. The ‘subprime mortgage’ crisis11 and the ‘bonfire of the quangos’12 depleted the political goodwill and public finances for speculative development work. Work that had previously and implicitly assumed the ‘diffusion of innovations’13 into mainstream provision. That ‘trickle down’ would take these ideas from pilot project to production line.

The Shift in Mobile Learning: What Changed?

Certainly not the political or economic climate, but mobile phones are now familiar, ubiquitous and powerful, and so is artificial intelligence (AI), also familiar, ubiquitous and powerful. Both of these technologies are outside educational institutions rather than confined within them. 

These earlier pilots and projects were basically ‘dumb’ systems, with no ‘intelligence’, drawing only on information previously loaded into their closed systems. Now, we have ‘intelligence’, we have AI and we have AI chatbots on mobile phones. However, currently, AI lacks context and cannot know or respond to the location, history, activity or behaviour of the learner and their mobile phone. Unfortunately, many current AI applications and chatbots are stateless and do not retain memory across interactions, and this represents a further challenge to any continuity.

The Possibilities of Mobile Learning: Could We Do Better Now?

Today’s network technologies can enable distributed connected contributions and consumption, enabling writing and reading. These might realise more of the possibilities of constructivism and social constructivism. They could enable educational systems to learn about and respond to their individual learners and their environment, connecting groups of learners and showing them how to support each other14. 

So, is there the possibility of convergence? Is it possible to combine the ‘intelligence’ of AI, the ‘memory’ of databases and the context provided by mobile phones, including both the learner and their environment? Could this be merged and mediated by educational expertise, acting as an interface between the three technologies, filtering, selecting and safeguarding?

What might this look like? We could start by adding ‘intelligence’ and ‘memory’ to our earlier examples.

The Future of Mobile Learning: What Could it Look Like? 

In terms of formal learning, our previous examples of the Uffizi Galleries, the Lake District, the Berlin Wall and Nottingham Castle are easy to extrapolate and imagine. Subject to a mediating educational layer, learners would each be in touch with other learners, helping each other in personalised versions of the same task. They could receive background information, ideas, recommendations, feedback and suggestions, cross-referenced with deadlines, schedules and assignments from their university LMS, all based on the cumulative history of their individual and social interactions and activities. 

When it comes to community learning or visitor attractions, systems could be created that encourage interactive, informal learning. For example, a living local history or 3D community poem spread around in the air, held together by links and folksonomies15, perhaps using tags to connect ideas, a living virtual world overlaying the real one. These systems could also support more prosaic purely educational applications, combining existing literary, artistic or historical sources with personal reactions or recollections.

Technically, this is about accessing the mobile phone’s contextual data, but sometimes other simple mobile data communications, for context. It also requires querying a relational database16 to retrieve history and constraints, and perhaps an institutional LMS, to retrieve assignments, timetables and course notes. AI can then be prompted to bring these together for some educational activity. Certainly, a proof of concept is eminently feasible. The expertise and experience of the three core disciplines are still out there and only need to be connected, tasked and funded.

Conclusions and Concerns

This piece sketches some broad educational possibilities once we enlist AI to support various earlier kinds of contextual mobile learning. Specific implementations and developments must address considerable social, legal, ethical and regulatory concerns and requirements. The earlier generation of projects might have already worked with these, privacy and surveillance being the obvious ones. Still, AI adds an enormous extra dimension to these, and there are other concerns like digital over-saturation, especially of children and vulnerable adults.

Nonetheless, this convergence of AI, contextual mobile data and educational expertise promises a future where learning is not confined to traditional settings but is a fluid, intelligent and deeply embedded aspect of our daily lives, making education more effective, accessible and aligned with individual and societal needs.

Mobile Learning & GenAI for the Less Privileged, Refugees & the Global South

How can mobile learning and GenAI reach those traditionally left out of educational innovation?

In a recent episode of Silver Lining for Learning, an award-winning webinar and podcast series, Prof. John Traxler joined a panel to discuss how mobile learning and generative AI can support less privileged learners, including refugees and communities in the Global South. 

The episode, ‘Mobile Learning & GenAI for the Less Privileged, Refugees & the Global South,’ builds on many of the questions raised in this article. It explores how mobile technologies have and haven’t fulfilled their potential, and what role GenAI might now play in addressing longstanding educational inequalities.

Watch the full episode:

  1. There is considerable literature, including:
    Special editions: Research in Learning Technology, Vol. 17, 2009. 
    Review articles: Kukulska-Hulme, A., Sharples, M., Milrad, M., Arnedillo-Sanchez, I. & Vavoula, G. (2009). Innovation in mobile learning: A European perspective. International Journal of Mobile and Blended Learning, 1(1), 13–35.
    Aguayo, C., Cochrane, T. & Narayan, V. (2017). Key themes in mobile learning: Prospects for learner-generated learning through AR and VR. Australasian Journal of Educational Technology, 33(6).
    Edited books: Traxler, J. & Kukulska-Hulme, A. (Eds) (2015), Mobile Learning: The Next Generation, New York: Routledge. (Also available in Arabic, 2019.) 
    More philosophically, Traxler, J. (2011) Context in a Wider Context, Medienpädagogik, Zeitschrift für Theorie und Praxis der Medienbildung. The Special Issue entitled Mobile Learning in Widening Contexts: Concepts and Cases (Eds.) N. Pachler, B. Bachmair & J. Cook, Vol. 19, pp. 1-16. ↩︎
  2. Meaning, ‘real-life’ settings. ↩︎
  3. Meaning, ‘real-life’ tasks. ↩︎
  4. Meaning, learning tailored to each separate individual learner.  ↩︎
  5. Educational technology researchers distinguish between what teachers say, what they ‘espouse’, and what they actually do, what they ‘enact’, usually something far more conservative or traditional. ↩︎
  6. An educational philosophy based on learners actively building their knowledge through experiences and interactions. ↩︎
  7. A variant of constructivism that believes that learning is created through social interactions and through collaboration with others. For an excellent summary of both, see: https://www.simplypsychology.org/constructivism.html  ↩︎
  8. For an explanation, see: https://smartasset.com/investing/the-economics-of-mobile-apps ↩︎
  9. A common term among computing professionals, referring to whether or not different systems, such as hardware, software, applications and peripherals, will actually work together, or whether it would be more like trying to fit a UK plug into an EU socket.  ↩︎
  10. A more detailed account is available at: https://medium.com/@Jisc/what-killed-the-mobile-learning-dream-8c97cf66dd3d ↩︎
  11. For an explanation, see:https://en.wikipedia.org/wiki/Subprime_mortgage_crisis ↩︎
  12. For an explanation, see: 2010 UK quango reforms – Wikipedia, which impacted Becta, the LSDA, Jisc and other edtech supporters.  ↩︎
  13. For an explanation, see: https://en.wikipedia.org/wiki/Diffusion_of_innovations ↩︎
  14. The proximity of physical or geographical context that the location awareness of neighbouring mobile phones could extend to embrace social proximity, meaning learners who are socially connected, or educational proximity, meaning learners working on similar tasks. The latter idea connects to the notions of ‘scaffolding’, ‘the more knowledgeable other’ and ‘the zone of proximal development’ of the theorist Vygotsky. For more, see: https://en.wikipedia.org/wiki/Zone_of_proximal_development ↩︎
  15. Databases conventionally have a fixed structure, for example, personal details based on forename, surname, house name, street name and so on, with no choice. Folksonomies, by contrast, are defined by the user, often on the fly. For example, tagging with labels such as ‘people I like’, ‘people nearby’, ‘people with a car’. Diigo, a social bookmarking service, uses tagging to implement a folksonomy. ↩︎
  16. Relational databases, unlike ‘flat’ databases based solely on a file, capture relationships, such as a teacher working in a college or a student enrolling in a course, and include all the various individual teachers, courses, students and colleges. ↩︎

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Create and Quiz: CEFR-Aligned AI Tools for Language Teaching

Posted on by Miguel Rivera

How can AI help language teachers save time, tailor materials and support learners at every level? In our latest Language Teaching Takeoff Webinar, we explored how two CEFR-aligned generators in TeacherMatic, Create a Text and Multiple Choice Questions, make creating relevant input and fast-track assessment easy. 

Create and Quiz: CEFR-Aligned AI Tools for Language Teaching

London, July 2025 – In the latest chapter of the Language Teaching Takeoff Webinar Series, ‘Generate, Engage and Assess: Create Custom Texts and Multiple Choice Quizzes’, award-winning educator and edtech consultant Nik Peachey guided participants through a live demo of two key generators particularly beneficial for language education: Create a Text and Multiple Choice Questions. 

Moderated by Giada Brisotto, Senior Marketing and Sales Operations Manager at Avallain, the session showed how language teachers can use TeacherMatic to generate original CEFR-level texts and instantly assess learner understanding. The examples explored during the session demonstrated how these tools support practical teaching needs without requiring any prompt engineering or AI expertise, all within an approach that prioritises ethics and safety.

Exploring the Value of Teacher-Controlled Content Generation

Nik began by highlighting how TeacherMatic differs from generic content tools. Built around classroom needs, the platform offers dozens of AI generators that help teachers plan, adapt and create lesson content. For language educators, CEFR alignment across tools ensures that outputs are suitable for specific levels, skills and teaching goals. 

Customised Texts for Every Level

With the Create a Text generator, teachers can define the topic, CEFR level, grammar focus and text type before generating a classroom-ready passage. Nik demonstrated how this can be used to create a short story, a dialogue or an informational text, depending on the teaching context. Teachers can also select the vocabulary focus or set a maximum word count to keep the text suitable for the target group.

The generator was created to facilitate differentiation, simplifying the process of adapting the same theme across various levels. It is beneficial for preparing writing models, reading texts, speaking prompts or listening scripts. If the output is not quite right, the teacher can instantly regenerate until the tone, length, or complexity matches their needs, with a simple click of the ‘Refine’ button, within the generator’s interface.

Instant Formative Assessment

The Multiple Choice Questions generator allows teachers to create CEFR-aligned quizzes using any text as input. This can include text generated within TeacherMatic, the teacher’s own materials, or content sourced from an external link. Nik illustrated how this tool can be used to generate quick comprehension checks, grammar quizzes or vocabulary reviews in just a few clicks. Once created, quizzes can be exported in multiple formats, including Kahoot, Excel and Word, or saved directly to Google Drive, giving educators flexible options for classroom delivery or sharing with learners.

Built for the Language Classroom

Both generators are part of the Language Teaching Edition of TeacherMatic, which provides tools specifically developed for CEFR-aligned teaching. These include level checkers, adaptation tools and generators for targeted vocabulary, grammar, speaking and writing tasks. The session reinforced how each feature supports everyday classroom needs, from content creation to assessment.

Reflecting on Impact

Participants left the session with practical ideas for incorporating these two featured generators in their daily work. Key benefits discussed included:

  • Creating original texts without having to search or adapt existing ones.
  • Quickly generating CEFR-aligned multiple-choice quizzes to check understanding.
  • Adapting the same theme across different CEFR levels.
  • Saving time while maintaining control over content quality.

By combining flexibility with pedagogical structure, the Create a Text and Multiple Choice Questions generators offer a practical way to generate, engage and assess across the language learning journey.

Explore the Language Teaching Edition of TeacherMatic

Whether teaching A1 learners or guiding advanced students through C1 material, the Language Teaching Edition of TeacherMatic helps you do it faster, better and more flexibly. 

Explore it here

Next in the Webinar Series

After a short summer pause, the Language Teaching Takeoff Webinar Series returns in September. Join us for the next session:

Date: Thursday, 11th September

Time: 12:00 – 12:30 BST | 13:00 – 13:30 CEST

Click here to register and save a seat

The topic will be announced soon and, as always, will focus on practical ways that AI can support language educators with CEFR-aligned tools. Register early to secure your spot.

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com 

Delivering Accessible Learning Experiences: Avallain’s Inclusive Design Approach

Posted on by Miguel Rivera

As the European Accessibility Act (EAA) deadline approaches, organisations delivering digital education must take decisive steps to ensure inclusivity. At Avallain, we’ve built accessibility into the core of our technology, empowering publishers, institutions and teachers to reach every learner, regardless of ability or context.

Delivering Accessible Learning Experiences: Avallain’s Inclusive Design Approach

St. Gallen, June 2025—The European Accessibility Act (EAA) will come into force on 28th June 2025, and educational organisations across the EU are preparing to meet a new legal standard for digital inclusion. For those offering digital education, this moment brings both the challenge of ensuring compliance and the opportunity to enable broader, fairer participation by removing barriers to participation.

At Avallain, we believe the long-standing commitment to accessibility should be guided by more than regulations. It reflects the belief that digital education should empower all learners. Through expert partnerships, rigorous audits and accessibility-first product design, we aim to enable the educational sector to meet and exceed the expectations set by the EAA.

Accessibility by Design: Supporting Legal Compliance and Learner Success

The EAA harmonises European accessibility requirements for a wide range of digital services, including e-learning content and platforms. Digital education tools must meet recognised standards such as the Web Content Accessibility Guidelines (WCAG) 2.1 AA.

Rather than viewing these requirements as a constraint, publishers and institutions can embrace them as a framework to deliver more inclusive, effective learning. Avallain’s accessibility strategy enables our partners to:

  • Reach broader audiences, including learners with disabilities and those using assistive technologies.
  • Increase platform usability and content clarity for all learners.
  • Build trust and credibility in competitive, regulated markets.

By integrating accessibility into every layer of our technology stack, we make compliance achievable and meaningful.

Avallain Author: Creating Accessible Content with Confidence

Avallain Author empowers education providers to develop digital content that meets the highest accessibility standards. Our built-in features allow content teams to create inclusive learning experiences at scale, without additional overhead.

Key capabilities include:

  • Keyboard and screen reader compatibility, enabling full navigation without a mouse.
  • AI-generated alt text for all visual elements, helping to support visually impaired learners.
  • AI-powered transcript and subtitle support for multimedia components.
  • Customisable layouts that adapt to various learning needs, such as high contrast and font scaling, are supported by Mercury Design Pack’s accessibility features.
  • Accessibility controls that inform content creators when media assets are compliant or have not met accessibility standards.
  • A dedicated Accessibility module within the Author Training & Certification course, guiding users through Avallain Author’s accessibility features and how to apply them effectively.

These comprehensive accessibility features ensure that content creators and academic staff can confidently publish content that aligns with WCAG 2.2 AA and is ready for any compliance audit.

Mercury Design Pack: Building Accessibility into Every Interaction

To guarantee accessibility at every touchpoint, Avallain’s Mercury Design Pack, the foundation of our user interface, has been purpose-built for inclusive learning journeys.

Its accessibility features include:

  • Strict adherence to WCAG 2.2 AA in every design element, from contrast ratios to focus states.
  • Component-level keyboard accessibility ensures seamless navigation across all interactive elements.
  • Scalable and readable typography, optimised for users with dyslexia and other reading differences.
  • Consistent UX behaviours help all learners feel confident and in control, especially those with cognitive challenges.

Critically for content creation, dozens of interactive activity types built with Mercury have already been audited and validated for accessibility. This allows publishers and authors to create rich, engaging learning experiences that are fully aligned with international accessibility standards without requiring any extra adaptation or technical overhead.

Avallain Magnet: Delivering Learning Without Barriers

Accessibility doesn’t stop at content. It must extend to the platforms where learning happens. Avallain Magnet, our out-of-the-box learning management system, ensures every user can engage confidently and independently.

With Avallain Magnet, schools and institutions benefit from:

  • Full screen reader support across teacher, learner and admin environments.
  • Colour and spacing customisation options, supporting neurodiverse learners and those with visual impairments.
  • Consistent keyboard navigation, allowing users to interact with the platform using only the keyboard.

These features are embedded by default, giving schools, institutions and teachers the peace of mind that their digital learning delivery is design-inclusive.

TeacherMatic: Helping Teachers Create Inclusive Materials Instantly

Accessibility must be effortless for individual educators. TeacherMatic, our AI toolkit designed for teachers, integrates accessibility best practices into every generator.

Whether users are creating quizzes, rubrics or complete lesson plans, TeacherMatic includes:

  • Inclusive activity design, incorporating Bloom’s taxonomy and Universal Design for Learning (UDL) principles.
  • Templates and content that consider learners with dyslexia, ADHD and other learning differences.
  • Time-saving tools so teachers don’t have to start from scratch and can instead focus on adapting materials for diverse needs.

By embedding inclusive defaults into content creation, TeacherMatic supports educators in safely delivering compliant, learner-centred instruction without the burden of technical know-how.

AI for Accessibility: The Mission of the Avallain Lab and Avallain Intelligence

Beyond compliance, Avallain invests in future-facing developments to expand what accessibility can mean in digital education. We explore how AI can actively support inclusion through our dedicated R&D arm, Avallain Lab, and our responsible AI framework, Avallain Intelligence.

This includes:

  • Collaborating with accessibility experts such as the Digital Accessibility Centre to evaluate and improve our products.
  • Embedding accessibility principles into our development cycles to ensure all innovations align with WCAG 2.2 AA standards.
  • Using AI responsibly to support content creation workflows, such as generating alt text, subtitles and transcripts to enhance media accessibility.

By integrating accessibility into every layer of our technology and development pipeline, we support industry stakeholders to meet evolving standards while staying focused on learner equity and inclusion.

Accessibility Is Everyone’s Future

As the EAA comes into force, accessibility has become a shared priority across the education landscape. For some, it’s a new legal requirement. For others, it’s a long-held value. For all, it’s an opportunity to create learning experiences that are fairer, broader and more impactful.

We believe in supporting publishers, schools, institutions, content creators and teachers in this journey to not just meet a legal standard but to set a new one. When learning is truly accessible, everyone benefits.

Visit our Accessibility page to learn more about Avallain’s approach to accessibility in education and download our latest Accessibility Conformance Report. 

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Avallain and Educate Ventures Research Collaborate to Deliver Robust, Real-World Guidance on Ethical AI in Education

Posted on by Miguel Rivera

‘From The Ground Up’ is a new report and research-based framework designed in line with Avallain Intelligence, our strategy for the responsible use of AI in education, and built with and for educators and institutions.

Avallain and Educate Ventures Research Collaborate to Deliver Robust, Real-World Guidance on Ethical AI in Education

St. Gallen, June 2025 – As generative AI transforms classrooms and educational workflows, clear, actionable ethical standards have never been more urgent. This is the challenge addressed in ‘From the Ground Up: Developing Standard Ethical Guidelines for AI Implementation in Education’, a new report developed by Educate Ventures Research in partnership with Avallain.

Drawing on extensive consultation with educators, multi-academy trusts, developers and policy specialists, the report introduces a practical framework of 12 ethical controls. These are designed to ensure that AI technologies align with educational values, enhance rather than replace human interaction and remain safe, fair and transparent in practice.

Unlike abstract policy statements, ‘From the Ground Up’ bases its guidance in classroom realities and product-level design. It offers publishers, institutions, content service providers and teachers a path forward that combines innovation with integrity.‘Since the beginning, we have believed that education technology must keep the human element at its core. This report reinforces that view by placing the experiences of teachers and learners at the centre of how we build, evaluate and implement AI. Our role is to ensure that innovation never comes at the cost of well-being, agency or trust, but instead strengthens the human connections that make learning meaningful.’ – Ursula Suter and Ignatz Heinz, Co-Founders of Avallain.

Download The Executive Version of The Report

A Framework Informed By The People It Serves

Developed over six months through research, case analysis, and structured stakeholder engagement, the report draws on input from multi-academy trust leaders, expert panels of educators, technologists and AI ethicists.

The result is a framework of 12 ethical controls:

  1. Learning Outcome Alignment
  2. User Agency Preservation
  3. Cultural Sensitivity and Inclusion
  4. Critical Thinking Promotion
  5. Transparent AI Limitations
  6. Adaptive Human Interaction Balance
  7. Impact Measurement Framework
  8. Ethical Use Training and Awareness
  9. Bias Detection and Fairness Assurance
  10. Emotional Intelligence and Well-being Safeguards
  11. Organisational Accountability & Governance
  12. Age-Appropriate & Safe Implementation

Each control includes a definition, challenges, mitigation strategies, implementation guidance and relevance to all key education stakeholders. The result is a practical, structured set of tools, not just principles.

‘This report exemplifies our mission at Educate Ventures Research and Avallain: to bridge the gap between academic research and real-world educational technology. By working closely with teachers, school leaders and developers, we’ve created ethical controls that are both grounded in evidence and practical in use. Our goal is to ensure that AI in education is not only effective, but also transparent, fair and aligned with the human values that define great teaching.’ – Prof. Rose Luckin, CEO of Educate Ventures Research and Avallain Advisory Board Member.

Recommendations That Speak To Real-World Risks

Some of the report’s most relevant insights include:

User Agency Preservation
AI should support, not override, the decisions of teachers and the autonomy of learners. Design should prioritise flexibility and transparency, allowing human control and informed decision-making.

Cultural Sensitivity and Inclusion
The report calls for continuous audits, bias detection and cultural representation in AI training data and outputs, with robust mechanisms for local adaptation.

Transparent AI Limitations
AI systems must explain what they can and cannot do. Visual cues, plain-language disclosures and in-context explanations all help users manage expectations.

Adaptive Human Interaction Balance
The rise of AI must not mean the erosion of dialogue. Thresholds for teacher-student and peer-to-peer interaction should be built into implementation plans, not left to chance.

Impact Measurement Framework
The report calls for combining short-term performance data and long-term qualitative indicators to assess whether AI tools genuinely support learning.

Relevance Across The Education Ecosystem

For Publishers

The report’s recommendations align closely with educational publishers’ strategic goals. Whether using AI to accelerate content production, localise materials, or personalise resources, ethical deployment requires more than efficiency. It requires governance structures that protect against bias, uphold academic rigour and enable human review. Solutions like Avallain Author already embed editorial control into AI-supported workflows, ensuring quality and trust remain paramount.

For Schools And Institutions

From primary schools to higher and vocational education providers, the pressure to adopt AI is growing. The report provides practical guidance on how to do so responsibly. It outlines how to set up oversight mechanisms, train staff, communicate transparently with parents and evaluate long-term impact. For institutions already exploring AI for tutoring or assessment, the controls offer a roadmap to stay aligned with safeguarding, inclusion and pedagogy.

For Content Service Providers

Agencies supporting publishers and ministries with learning design, editorial production and localisation will find clear implications throughout the report. From building inclusive datasets to ensuring transparent output verification, ethical AI becomes a shared responsibility across the value chain. Avallain’s technology, driven by Avallain Intelligence, enables these partners to apply ethical filters and maintain editorial standards at scale.

For Teachers

Educators are frontline decision makers. They shape how AI is used in the classroom. The report explicitly calls for User Agency Preservation to be maintained, Ethical Use Training and Awareness to be prioritised and teacher feedback to guide AI evolution. Solutions within Avallain’s portfolio, such as TeacherMatic, are already embedding these principles by offering editable outputs, contextual prompts and transparency in how each suggestion is generated.

The Role Of Avallain Intelligence: Putting Ethical Controls Into Action

Avallain Intelligence is Avallain’s strategy for the ethical and safe implementation of AI in education and the applied framework that aims to integrate these 12 ethical controls. It adheres to principles such as transparency, fairness, accessibility and agency within the core infrastructure of Avallain’s digital solutions.

This includes:

  • Explainable interfaces that clarify how AI decisions are made.
  • Editable content outputs that preserve user control.
  • Cultural customisation features for inclusive learning contexts.
  • Bias Detection and Fairness Assurance systems with review mechanisms.
  • Built-in feedback loops to refine AI based on classroom realities.

Avallain Intelligence was developed to meet and exceed the expectations outlined in ‘From the Ground Up’. This means publishers, teachers, service providers and institutions using Avallain tools are not starting from scratch but are already working within an ecosystem designed for ethical AI.

The work of the Avallain Lab, our in-house academic and pedagogical hub, continuously informs these principles and ensures that every advancement is grounded in research, ethics and real classroom needs.

‘The insights and methodology that underpin this report reflect the foundational work of the Avallain Lab and our commitment to research-led development. By aligning ethical guidance with practical use cases, we ensure that Avallain Intelligence evolves in direct response to real pedagogical needs. This collaboration shows how rigorous academic frameworks can inform responsible AI design and help create tools that are not only innovative but also educationally sound and trustworthy.’ – Carles Vidal, Business Director of the Avallain Lab. 

Download The Executive Version

This is a practical roadmap for anyone seeking to navigate the opportunities and risks of AI in education with clarity, confidence and care.

Whether you are a publisher exploring AI-powered content workflows, a school leader integrating new technologies into classrooms or a teacher looking for trusted guidance, ‘From the Ground Up’ offers research-based recommendations you can act on today.

Click here to download the executive version of the report to explore how the 12 ethical controls can help your organisation adopt AI responsibly, support educators, protect learners and remain committed to your educational mission.

About Educate Ventures Research

Educate Ventures Research (EVR) is an innovative boutique consultancy and training provider dedicated to helping education organisations leverage AI to unlock insights, enhance learning and drive positive outcomes and impact.​

Its mission is to empower people to use AI safely to learn and thrive. EVR envisions a society in which intelligent, evidence-informed learning tools enable everyone to fulfil their potential, regardless of background, ability or context. Through its research, frameworks and partnerships, EVR continues to shape how AI can serve as a trusted companion in teaching and learning.

Find out more at educateventures.com

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

_

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Thinking about the Edtech Echo Chamber

Posted on by Miguel Rivera

Educational technology is often seen as a straightforward solution to teaching challenges. Yet, beneath the surface lies a complex dynamic. Who ultimately shapes educational technology? This piece explores the proximity between those who buy and sell edtech and the gap between these decision-makers and those who actually use it. This imbalance influences both innovation and pedagogy. 

Thinking about the Edtech Echo Chamber

Author: Prof John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

Since joining Avallain and whilst continuing to work as a university professor, I have been reflecting on the nature of the edtech environment. My perspective is not only very generalised, subjective and impressionistic. It also overlooks major disturbances, most obviously the global pandemic, the alleged ‘pivot’ to digital learning and the global explosion of artificial intelligence, with its haphazard adoption in education.

Specifically, I have been thinking about the small informal community of people within the organisations of the education sectors who design, develop and sell dedicated edtech systems and other people who buy, install and maintain such systems. On behalf of their respective organisations, they are engaged in transactions that are highly focused, highly technical, highly complex and highly responsible. The members of this informal community, both ‘buyers’ and ‘sellers’, must, by the nature of their enormous expertise, share very similar backgrounds, values, language, ideas and influential personalities in order to be effective. Their experience suggests that in their careers they can change from ‘sellers’ or ‘buyers’ and back again several times. 

I suspect that they share a kind of groupthink that seems, certainly in their terms, to be productive, objective and transparent. By this, I mean that the buyers and sellers agree on what they should be discussing (and what not to discuss). This groupthink determines the direction of procurement and consequently focuses on making existing products and systems faster, bigger, cheaper, more secure, more attractive and more compliant, and builds on current perceived successes. 

The User Community

There is, however, another informal community involved, on the periphery of the informal edtech buyers and sellers community, namely that of teachers, lecturers, learners and students.

My worry is that because of differences in values, language, ideas and influential personalities, any discourse with these communities of teachers, lecturers, learners or students is much less efficient and effective. It is often perceived as partly mutually incomprehensible, characterised by one community or the other using concepts, methods, tools, values and references not wholly or confidently understood by the other.

As an example, many organisations using educational technology are trying to address equity, inclusion and diversity in their provision and their ethos. They may also be trying to promote different models or strategies for teaching and learning. Whilst the communities of teachers and lecturers know whom to involve to advance these initiatives within their own work, moving upstream and being able to articulate their needs in technically meaningful ways seems generally much more difficult. There is a chasm between ‘academic’ departments, doing the teaching, and ‘service’ departments, running the digital technology.

Obviously, issues like staff retraining, interoperability and managerial nervousness further limit the scope for systemic, as opposed to incremental, change. So do the business models of educational organisations and, for example, of education and academic publishers.

Horizon Scanning

I did consultancy for the UK NHS, National Health Service, some years ago, helping to improve their edtech ‘horizon scanning’ capacity, and whilst it is possible to develop methods and tools for this, I now worry that the problem is the possible inability to break out of the groupthink, out of the accepted views, of the community in question. At the time, I expressed this slightly differently, saying it was easy to see innovations on the horizon coming straight at you, but the challenge was to spot the relevance of those on the horizon, appearing further off to the left or way off to the right. Again, there is a difference between ‘hard’ technical stuff on the horizon and ‘soft’ educational stuff.  

There might be a connection between these observations about horizon scanning and other work on tools and methods to support brainstorming, which attempt to generate new ideas within a community as opposed to recognising ideas outside the community and on the horizon.  

I might be equating the groupthink of various closed but informal groups with the ideas about paradigms, scientific or otherwise, but in a practical sense, I wondered how we promote the ‘paradigm shifts’ that bring about dramatic but benign or beneficial transformation. In short, where do new products come from?

Breaking the Edtech Echo Chamber

In conclusion, I am attempting to make a case that the people buying and selling educational technology often understand each other much better than they understand the people using it, and thus educational technology is driven by technology push (or technological determinism) rather than pedagogy pull. 

I think this builds in some pedagogic conservatism. There might be other reasons or perspectives, but this gap remains a critical challenge. 

The future of educational technology depends on breaking down silos and aligning the expertise of buyers and sellers with the lived needs of educators and learners. Together, fostering shared language and values will empower all stakeholders to participate in shaping tools that genuinely enhance education.

1 Perhaps this current piece could be reworked to address these two issues but I think both have served to reinforce existing attitudes and values, and that pronouncements of systemic transformation may be premature or overstated or misleading.

2 But clearly this can only be impressions and could never be based on anything purporting to be ‘scientific’ or ‘objective’. 

3 I think in fact I am saying this community articulates and represents a ‘paradigm’ as defined by Thomas S. Kuhn in his 1974 short paper Second Thoughts on Paradigms (available online at https://uomustansiriyah.edu.iq/media/lectures/10/10_2019_02_17!07_45_06_PM.pdf), albeit a modest one compared to Darwinian evolution, heliocentric astronomy or even object-oriented programming.

4 There is also a factor understood in requirements engineering about the human incapacity to answer questions about the future; ask customers or users what they would like in the future and they will reply, what they already have but faster. This too builds in conservatism. Fortunately, there are various better techniques to elicit future requirements from customers or users. 

5 Characterised on one side by fairly generalised, abstract and social ideas and values and on the other by specific, concrete and technical ideas and values, though it is difficult for this characterisation to be objective and neutral.

6 It could be the grand ‘connectivist’ conceptions of the early ideologically driven MOOCs or merely flipped learning, self-directed learning, critical digital literacy, project-based learning, situated learning and so on.

7 Which might explain why most universities and colleges seem stuck in the digital technology of the 1990s, namely the VLE/LMS and the networked desktop computer, in spite of the ubiquity of social media and personal technologies.

8 Defined here as the ability of different hardware and software systems with different roles within a complex organisation to work together.

9 ‘Horizon scanning’ is the activity of intercepting and interpreting ideas that are emergent, unformed, unclear and then seeing their practical relevance ahead of colleagues and competitors. There are various methods and for the NHS we attempted to synthesise and validate a method from those already in government departments, universities and corporations.

10 Thinking of Teflon and Post-Its.

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_ 

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Smarter Content for Every Level: CEFR Adaptation and Alignment with TeacherMatic

Posted on by Miguel Rivera

Explore insights from the latest TeacherMatic Language Teaching Takeoff Webinar with Nik Peachey, during which he demonstrated how AI can streamline CEFR adaptation, support differentiated instruction and make CEFR alignment practical and achievable.

Smarter Content for Every Level: CEFR Adaptation and Alignment with TeacherMatic

London, June 2025 – In the latest TeacherMatic Language Teaching Takeoff Webinar, ‘Adapting Content for Effective CEFR-Aligned Language Teaching’, award-winning educator and edtech consultant Nik Peachey demonstrated how AI can transform the way teachers align content to the CEFR framework. Moderated by Giada Brisotto, Senior Marketing & Sales Operations Manager at Avallain, the session introduced teachers to time-saving tools specifically tailored to language classrooms.

Designing for Language Classrooms, Not Just Outputs

Unlike generic AI tools that produce unpredictable or overly complex content, the TeacherMatic Language Teaching Edition is built around a core understanding of language teaching workflows. Nik opened the session with an overview of the platform and shared how it was purpose-built for educators:

‘These generators aren’t just text tools. They’re designed with real classroom needs in mind. You input your goals, level and theme, and the results are ready to use or refine.’

Developed in collaboration with educators, the platform includes over 40 AI generators that can adapt reading texts, create scaffolded tasks and provide differentiated resources based on CEFR levels.

Exploring the Generators

To help educators address common challenges, Nik focused the session on two powerful generators:

Adapt Your Content Generator

This generator allows teachers to input and adjust a text to a different CEFR level. It’s beneficial for mixed-ability groups, enabling the creation of simpler or more advanced versions of the same content, without changing the theme.

‘You can take something at B2 and make it work for A2 in seconds, and the results aren’t just accurate. They’re pedagogically useful.’ – Nik Peachey.

CEFR Level Checker

This tool allows teachers to paste or upload text and instantly receive a detailed CEFR analysis. It provides an overall level and a breakdown of linguistic features such as vocabulary and grammar complexity.

‘It’s great for checking the level of materials you’re designing for your students. It can also be used to analyse students’ written work or evaluate authentic texts pulled from the internet.’ – Nik Peachey.

Adapting to Your Students

Nik highlighted the platform’s flexibility as one of its key advantages. Whether dealing with mixed-ability groups or looking to differentiate instruction, you can adapt materials instantly.

‘It’s not just about saving time. It’s about creating something that actually works for your learners faster.’ – Nik Peachey.

Teachers can generate different versions of the same resource for different groups. With just a few clicks, a B1 reading passage can be simplified to A2 or made more challenging for B2 learners.

Built with Teachers in Mind

Unlike general-purpose AI platforms, the TeacherMatic Language Teaching Edition generators are fine-tuned for language education. Nik explained that the behind-the-scenes work of AI developers ensures consistency and relevance in every output.

The CEFR Level Checker, for instance, has been rigorously tested to provide more accurate results than a generic prompt. This design means less trial and error and more reliable results for teachers pressed for time.

Real Concerns, Real Solutions

Attendees asked about prompt quality, language combinations and how CEFR logic is applied behind the scenes. Nik explained that while CEFR is a flexible framework, the generators have been carefully built around the functional descriptors educators rely on most. Giada added that this work is supported by a collaboration with NILE and CEFR expert Helen Boyd to ensure rigorous alignment that reflects academic best practice.

Nik also encouraged teachers to review outputs with their own learners in mind. The platform is a tool, not a finished product. It empowers educators to shape content, not simply consume it.

Explore the TeacherMatic Language Teaching Edition

Whether teaching A1 learners or guiding advanced students through C1 material, the TeacherMatic Language Teaching Edition helps you do it faster, better and more flexibly. 

Explore it here

Next in the Webinar Series

Don’t miss the next session of the TeacherMatic Language Teaching Takeoff series, ‘Generate, Engage and Assess: Create Custom Texts and Multiple Choice Quizzes‘:

  • Date: Thursday, 10th July
  • Time: 12:00 – 12:30 BST | 13:00 – 13:30 CEST

Register here

This session will show you how to use AI to generate engaging texts and effective quiz-based assessments. It is perfect for educators looking to enrich their classrooms with authentic, CEFR-aligned materials in minutes.

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_ 

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

Who Owns ‘Truth’ in the Age of Educational GenAI?

Posted on by Lindsay Dowling

As generative AI becomes more deeply embedded in digital education, it no longer simply delivers knowledge; it shapes it. What counts as truth, and whose truth is represented, becomes increasingly complex. Rather than offering fixed answers, this piece challenges educational technologists to confront the ethical tensions and contextual sensitivities that now define digital learning.

Who Owns ‘Truth’ in the Age of Educational GenAI?

Author: Prof. John Traxler, UNESCO Chair, Commonwealth of Learning Chair and Academic Director of the Avallain Lab

St. Galen, May 23, 2025 – Idealistically, perhaps, teaching and learning are about sharing truths, and sharing facts, values, ideas and opinions. Over the past three decades, digital technology has been increasingly involved or implicated in teaching and learning, and increasingly involved or implicated in shaping the truths, the facts, values, ideas and opinions that are shared. Truth seems increasingly less absolute, stable and reliable and digital technology seems increasingly less neutral and passive.

The emergence of powerful and easily available AI, both inside education and in the societies outside it, only amplifies and accelerates the instabilities and uncertainties around truth, making it far less convincing for educational digital technologists to stand aside, hoping that research or legislation or public opinion will understand the difficulties and make the rules. This piece unpacks these sometimes controversial and uncomfortable propositions, providing no easy answers but perhaps clarifying the questions.

Truth and The Digital

Truth is always tricky. It is getting trickier and trickier, and faster and faster. We trade in truth, we all trade in truth; it is the foundation of our communities and our companies, our relationships and our transactions. It is the basis on which we teach and learn, we understand and we act. And we need to trust it.

The last two decades have, however, seen the phrases ‘fake news’ and ‘post truth’ used to make assertions and counter assertions in public spheres, physical and digital, insidiously reinforcing the notion that truth is subjective, that everyone has their own truth. It just needs to be shouted loudest. These two decades also saw the emergence and visibility of communities, big and small, in social media, able to coalesce around their own specific beliefs, their own truths, some benign, many malign, but all claiming their adherents to be truths. 

The digital was conceived ideally as separate and neutral. It was just the plumbing, the pipes and the reservoirs that stored and transferred truths, from custodian or creator to consumers, from teacher to learner. Social media, intrusive, pervasive and universal, changed that, hosting all those different communities.

The following selection of assertions comprises some widely accepted truths, though this will always depend on the community; others are generally recognised as false and some, the most problematic, generate profound disagreement and discomfort.

  • The moon is blue cheese, the Earth is flat
  • God exists
  • Smoking is harmless
  • The holocaust never happened 
  • Prostate cancer testing is unreliable
  • Gay marriage is normal 
  • Climate change isn’t real 
  • Evolution is fake
  • Santa Claus exists 
  • Assisted dying is a valid option
  • Women and men are equal
  • The sun will rise
  • Dangerous adventure sports are character-building
  • Colonialism was a force for progress

These can all be found on the internet somewhere and all represent the data upon which GenAI is trained as it harvests the world’s digital resources. Whether or not each is conceived as true depends on the community or culture.

Saying, ‘It all depends on what you mean by …’ ignores the fundamental issue, and yes, some may be merely circular while others may allow some prevarication and hair-splitting, but they all exist. 

Educational GenAI

In terms of the ethics of educational AI, extreme assertions like the ‘sun will rise’ or ‘the moon is blue cheese’ are not a challenge. If a teacher wants to use educational GenAI tools to produce teaching materials that make such assertions, the response is unequivocal; it is either ‘here are your teaching materials’ or ‘sorry, we can’t support you making that assertion to your pupils’.   

Where educational AI needs much more development is in dealing with assertions which, for us, may describe non-controversial truths, such as ‘women and men are equal’ and ‘gay marriage is normal’, but which may be met by different cultures and communities with violently different opinions.

GenAI harvests the world’s digital resources, regurgitating them as plausible, and in doing so, captures all the prejudice, biases, half-truths and fake news already out there in those digital resources. The role of educational GenAI tools is to mediate and moderate these resources in the interests of truth and safety, but we argue that this is not straightforward. If we know more about learners’ culture and contexts and their countries, we are more likely to provide resources with which they are comfortable, even if we are not. 

Who Do We Believe?

Unfortunately, some existing authorities that might have helped, guided and adjudicated these questions are less useful than previously. The speed and power of GenAI have overwhelmed and overtaken them. 

Regulation and guidance have often mixed pre-existing concerns about data security with assorted general principles and haphazard examples of their application, all focused on education in the education system rather than learning outside it. The education system has, in any case, been distracted by concerns about plagiarism and has not yet addressed the long-term issues of ensuring school-leavers and graduates flourish and prosper in societies and economies where AI is already ubiquitous, pervasive, intrusive and often unnoticed. In any case, the treatment of minority communities or cultures within education systems may itself already be problematic.

Education systems exist within political systems. We have to acknowledge that digital technologies, including educational digital technologies, have become more overtly politicised as global digital corporations and powerful presidents have become more closely aligned.

Meanwhile, the conventional cycle of research funding, delivery, reflection and publication is sluggish compared to developments in GenAI. Opinions and anecdotes in blogs and media have instead filled the appetite for findings, evaluations, judgments and positions. Likewise, the conventional cycle of guidance, training, and regulation is slow, and many of the outputs have been muddled and generalised. Abstract theoretical critiques have not always had a chance to engage with practical experiences and technical developments, often leading to evangelical enthusiasm or apocalyptic predictions. 

So, educational technologists working with GenAI may have little adequate guidance or regulation for the foreseeable future.

Why is This Important?

Educational technologists are no longer bystanders, merely supplying and servicing the pipes and reservoirs of education. Educational technologists have become essential intermediaries, bridging the gap between the raw capabilities of GenAI, which are often indiscriminate, and the diverse needs, cultures and communities of learners. Ensuring learners’ safe access to truth is, however, not straightforward since both truth and safety are relative and changeable, and so educational technologists strive to add progressively more sensitivity and safety to truths for learners. 

At the Avallain Lab, aligned with Avallain Intelligence, our broader AI strategy, we began a thorough and ongoing programme of building ethics controls that identify what are almost universally agreed to be harmful and unacceptable assertions. We aim to enhance our use of educational GenAI in Avallain systems to represent our core values, while recognising that although principles for trustworthy AI may be universal, the ways they manifest can vary from context to context, posing a challenge for GenAI tools. This issue can be mitigated through human intervention, reinforcing the importance of teachers and educators. Furthermore, GenAI tools must be more responsive to local contexts, a responsibility that lies with AI systems deployers and suppliers. While no solution can fully resolve society’s evolving controversies, we are committed to staying ahead in anticipating and responding to them.

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

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Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com

From Rubrics to Results: Making Feedback More Impactful with AI in Language Teaching

Posted on by Lindsay Dowling

Delivering impactful feedback can be one of the most time-consuming parts of language teaching. In this chapter of the Language Teaching Takeoff Webinar Series, we explored how to streamline the feedback process without compromising the quality that learners deserve.

From Rubrics to Results: Making Feedback More Impactful with AI in Language Teaching

London, May 2025 – On May 15th, the Avallain Group hosted the second session in its Language Teaching Takeoff Webinar Series, ‘From Rubrics to Results: How to Provide Impactful Feedback’. The session was moderated by Giada Brisotto, Senior Marketing and Sales Operations Manager at Avallain, and led by Nik Peachey, educator, author and edtech consultant. 

This 30-minute session focused on how the Feedback Generator in the TeacherMatic Language Teaching Edition can assist educators in providing better, faster and more personalised feedback.

The Challenge: High-Quality Feedback Takes Time

Feedback is essential for student progress, but for teachers, it often comes at the cost of time and energy. Nik opened the session by acknowledging this widespread issue and proposing a practical, AI-supported solution: the Feedback Generator.

Unlike general-purpose tools, the TeacherMatic Feedback Generator, designed specifically for language teaching, allows educators to produce constructive feedback that aligns with assignment briefs, CEFR levels and specific pedagogical approaches.

Personalised Feedback at Scale

Nik demonstrated how the Feedback Generator makes it possible to maintain personalisation, even with large groups of students. By inputting a student’s response and the original task prompt, teachers can instantly generate comments that are:

  • Aligned with CEFR levels and subscales. (e.g., B1 writing > coherence and cohesion)
  • Tailored to the assessment criteria or rubric used by the teacher or institution.
  • Balanced between strengths and areas of improvement.

​​The result: fast, personalised and pedagogically relevant feedback.

Designed for Language Teachers, Not Just Generic Use

As it is purpose-built for language educators, the Feedback Generator supports core pedagogical models including:

  • Communicative Language Teaching (CLT)
  • Task-Based Learning (TBL)
  • Presentation Practice Production (PPP)
  • Lexical Approach
  • Test – Teach – Test

This flexibility allows teachers to generate feedback that fits their existing lesson models and institutional standards.

From Feedback to Feedforward

Nik emphasised that effective feedback not only reflects on the past but also guides learners as they progress. The Feedback Generator enables this by including next steps and actionable guidance in the comments, which can be adjusted for tone, focus and complexity.

This ‘forward approach’ aligns with current thinking in language assessment, that feedback should help students take ownership of their progress and better understand learning objectives.

Why It Matters: Lighter Workload, Deeper Impact

The session closed with a powerful reminder: when tools are designed around the real needs of teachers, not just general AI capabilities, they can genuinely reduce pressure without lowering standards.

By using the Feedback Generator, teachers can:

  • Save time without sacrificing quality
  • Ensure consistency in grading
  • Focus more on student support and less on repetitive admin
  • Promote deeper engagement with learning goals

What’s Next in the Series?

The Language Teaching Takeoff Webinar Series continues in June with ‘Adapting Content for Effective CEFR-Aligned Language Teaching’. You can reserve your seat now. This is a free webinar, but spaces are limited.

Save the Date:

  • Thursday, 12th June
  • 12:00 – 12:30 BST | 13:00 – 13:30 CEST

Register now for the webinar

Discover the TeacherMatic Language Teaching Edition

The Language Teaching Edition of TeacherMatic has been purpose-built to elevate language teaching and learning through sector-specific features designed for real classroom needs. With CEFR-aligned AI generators and support for key pedagogical models such as CLT, Task-Based Learning, PPP and more, it empowers language educators to create high-quality, personalised content efficiently and confidently.

Visit the dedicated landing page to explore all features in depth

About Avallain

At Avallain, we are on a mission to reshape the future of education through technology. We create customisable digital education solutions that empower educators and engage learners around the world. With a focus on accessibility and user-centred design, powered by AI and cutting-edge technology, we strive to make education engaging, effective and inclusive.

Find out more at avallain.com

About TeacherMatic

TeacherMatic, a part of the Avallain Group since 2024, is a ready-to-go AI toolkit for teachers that saves hours of lesson preparation by using scores of AI generators to create flexible lesson plans, worksheets, quizzes and more.

Find out more at teachermatic.com

_ 

Contact:

Daniel Seuling

VP Client Relations & Marketing

dseuling@avallain.com