Geeknovatorworld

03/10/2026

“Does this technology improve learning?” is a question we should ask. But how much does it leave out?

I am reading Neil Selwyn’s 2010 paper, Looking Beyond Learning, with generative AI in mind.

Selwyn argues that educational technology research needs to pay closer attention to what happens when a tool enters an actual school, university, or classroom.

The promises surrounding a technology can tell us very little about how people experience it under everyday conditions.

A tool enters a setting already shaped by budgets, policies, workloads, relationships, and unequal access.

Teachers and students negotiate its use within those conditions. Some adapt it. Some use it in unexpected ways. Some choose not to use it.

Those choices deserve investigation. We should be careful about treating every instance of non-use as a problem with the teacher or learner.

Selwyn proposes three questions that I find useful:

• What is technology use actually like in this setting?
• Why does it take this form?
• What are its consequences?

Applied to AI, these questions invite us to examine who does the checking, whose work becomes visible, whose judgement carries authority, and who can challenge a decision about the technology.

That application is my reading of the paper. Selwyn was writing about educational technology broadly, well before today’s generative AI tools.

His argument also asks us to look beyond the individual classroom. Institutional decisions and commercial interests help shape which tools become available and what counts as successful use.

This is a conceptual paper, offering a complementary perspective to research on learning effectiveness. It asks us to examine fairness, power, and participation alongside educational outcomes.

Author ✍🏽 Educator Technology

Reference:
Selwyn, N. (2010). Looking beyond learning: Notes towards the critical study of educational technology. Journal of Computer Assisted Learning, 26 (1), 65–73.

25/06/2026

Are you a student or young professional looking to expand your knowledge of Intellectual Property and Technology Transfer?

Applications are now open for the 2026 Annual Summer School, hosted by the World Intellectual Property Organization (WIPO) Academy, the Department of
Science, Technology and Innovation (through the National Intellectual Property
Management Office), the Companies and Intellectual Property Commission (CIPC)
and the University of Cape Town (UCT).

The 10-day programme will run from 23 November to 4 December 2026 in a hybrid format, allowing participants to attend either in person or online. Participation is free, with all tuition fees fully waived.

Read more: https://bit.ly/4oI8yf4

Research Chair: Intellectual Property, Innovation and Development Contracts and Innovation

20/06/2026

An interesting early resource that maps the terrain of AI in education before the emergence of generative AI.

The Council of Europe report looks at AI through three much deeper lenses: human rights, democracy, and the rule of law.

The report makes an important distinction between three areas:

1. Learning with AI: using AI tools such as tutoring systems, chatbots, writing feedback tools, adaptive platforms, and learning analytics.

2. Learning about learning: using learner data to analyze progress, predict risks, or understand learning patterns.

3. Learning about AI: helping students develop AI literacy, not only in a technical sense, but also in a human, ethical, and democratic sense.

the report also emphasizes the idea that AI literacy should never be reduced to coding, algorithms, or machine learning. Students also need to understand bias, surveillance, privacy, data rights, automated decision-making, commercial interests, human agency, and the social consequences of AI.

In other words, AI literacy must include the human dimension.

The report is especially critical of the way many AI tools are promoted in education. We often hear promises of personalization, efficiency, and better outcomes. But the report reminds us that many AI systems still rely on narrow pedagogies. They often guide students through pre-set pathways, track their behaviour, collect data, and make predictions about their learning.

That may look personalized on the surface. But personalization is not the same as human development.

The report also raises serious concerns about surveillance in schools: e-proctoring, emotion detection, attention tracking, biometric monitoring, and digital safeguarding systems that scan students’ activity.

These tools are often introduced in the name of safety, integrity, or efficiency, but they can normalize constant monitoring and weaken students’ privacy, autonomy, and trust.

This is not a small issue.

When children learn under continuous surveillance, they do not simply learn content. They also learn what kind of society they are expected to live in.

The report also warns against “AIED colonialism,” where AI tools developed mainly in wealthy, Western, English-speaking contexts are exported into different educational systems around the world. These tools may carry assumptions about language, knowledge, pedagogy, culture, and learning that do not fit local contexts.

Link in the first comment!

Reference:
Holmes, W., Persson, J., Chounta, I.-A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe.

14/06/2026

Critical AI Literacy Needs to Move Beyond Prompting!

I came across this Open University framework on Critical AI Literacy, and I think it offers a useful way to expand how we talk about AI in education.

Too much of the current conversation still treats AI literacy as a technical skill.

Can students write better prompts?

Can they evaluate AI outputs?

Can they use the tools productively?

All of that matters, but this framework goes further. It places Critical AI Literacy through the lens of equality, diversity, inclusion, and accessibility.

Students need to understand what AI can do, but they also need to question whose knowledge gets amplified, whose voices get marginalized, where bias enters the system, and how AI use affects privacy, agency, labour, sustainability, and future work.

The framework organizes Critical AI Literacy around six areas:

AI concepts and applications

Learning and teaching with AI

AI creativity

AI ethics

AI in society

AI careers

AI literacy should help learners use AI, critique AI, question AI, and make responsible decisions with AI. Anything less is just tool training.

References

Hauck, M., Moore, E., & Wright, C. (2025). A framework for the learning and teaching of critical AI literacy skills. The Open University.

Link 👇🏾

https://www.open.ac.uk/blogs/learning-design/wp-content/uploads/2025/01/OU-Critical-AI-Literacy-framework-2025-external-sharing.pdf



✍🏽 Technology Educators

08/06/2026

Turnitin has become a lucrative business by selling the promise of certainty in an age of AI uncertainty.

But the evidence keeps reminding us that AI detection is not as straightforward as many institutions would like to believe.

I recently read Lucky E. Atamhenwan’s 2026 paper, How are combinations of human-written words and LLM-generated words by ChatGPT, Copilot, Gemini and Grammarly detected by Turnitin?, and the findings are important for anyone involved in teaching, assessment, or academic integrity.

The study tested 81 scripts with different combinations of human-written and LLM-generated text, ranging from 100% human-written to 100% AI-generated. The AI-generated text came from ChatGPT, Copilot, Gemini, and Grammarly.

One of the key findings is that Turnitin did not flag fully human-written texts. It also did not produce AI scores when only 5% or 10% of the text was AI-generated.

But once AI-generated content reached around 15%, Turnitin began producing AI scores. The problem is that those percentages were often inaccurate.

At lower levels of AI-generated text, Turnitin tended to overestimate AI use.

At higher levels of AI-generated text, Turnitin tended to underestimate AI use.

Even more striking, detection varied depending on the tool used. ChatGPT-generated text, for instance, was often underdetected. In one case, a script that was 100% ChatGPT-generated received a Turnitin AI score of only 60%.

The study also showed that some “humanizing” or paraphrasing tools can significantly reduce detection. In some cases, Turnitin returned 0% AI scores for texts that were originally 100% LLM-generated and then humanized.

AI detectors may provide a signal, but they should never be treated as proof. A Turnitin AI score is not a verdict and should never be a confession!

I think instead of asking: “Was AI used?”, we better ask: “How was AI used, why was it used, and does that use align with the learning goals and policy expectations?”

Reference:
Atamhenwan, L. E. (2026). How are combinations of human-written words and LLM-generated words by ChatGPT, Copilot, Gemini and Grammarly detected by Turnitin? Education and Information Technologies.

✍🏽 Technology Education

01/06/2026

If you haven’t read UNESCO’s AI Competency Framework for Students, it is definitely a must-read.

UNESCO organizes student AI competency around four major areas:

Human-centred mindset
Ethics of AI
AI techniques and applications
AI system design

This matters because students are not just future users of AI. They are future citizens, workers, creators, decision-makers, and, in some cases, designers of AI systems. They need to understand how AI works, but they also need to understand its limits, risks, ethical implications, and social impact.

One of the strongest ideas in the framework is the emphasis on human agency. Students need to learn that AI should support (not substitute) human thinking. They also need to know when AI should not be used, especially in high-stakes decisions.

The framework also reminds us that AI literacy must include ethics, fairness, transparency, sustainability, and responsibility. This is especially important now, as many students are learning about AI through apps, platforms, social media, and trial and error rather than through intentional classroom instruction.

For educators, this framework gives us a useful starting point. It helps us move beyond the narrow question of “How can students use AI?” toward deeper questions:

How does AI shape thinking?
Who benefits from AI systems?
Who might be harmed?
How can students evaluate AI outputs critically?
How can they use AI responsibly and creatively?

01/06/2026

01/06/2026