Geeknovatorworld

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

18/04/2026

Technology and Inclusive Education!

Here is an interesting systematic review by Navas-Bonilla et al. (2025) mapping 159 studies on technology and inclusive education. The results tell us where the research community focuses, and where it doesn't.

60% of studies address sensory disabilities. Physical and intellectual disabilities each appear in 34%. Socioeconomic inclusion shows up in just 28%, and language barriers in only 8%.

The authors argue that inclusion goes beyond disability. Age, gender, health, language, socioeconomic status, all create barriers to learning. I agree completely.

The finding that struck me most is about teachers. Across all 159 studies, teacher training is the biggest barrier, not the technology itself.

Schools hand out tools without building capacity. This tracks with Celik's Intelligent-TPACK work (2023): teachers need specific competencies for technology.

There's a painful irony too. Students in rural and low-income areas lack the infrastructure to use the very tools designed to include them.

The other gap is generative AI. The review covers 2019-2024, so most studies predate ChatGPT going mainstream. A student with dyslexia today can ask a conversational AI to explain a concept seventeen different ways. That changes the inclusion equation, and this review can't account for it.

It's a solid map of where we've been. The work now is building the pedagogy for where we are.

Link in the first comment!



References

Navas-Bonilla, C. del R., Guerra-Arango, J. A., Oviedo-Guado, D. A., & Murillo-Noriega, D. E. (2025). Inclusive education through technology: A systematic review of types, tools and characteristics. Frontiers in Education, 10, 1527851.

✍🏽 Technology Teachers

31/12/2025

✍🏽 AI Research for Teachers and Educators