Med Kharbach

When does AI assistance become substantial enough to disclose, and how much oversight should researchers exercise when an AI agent carries out research tasks?

The title of Robert Sparrow and Gene Flenady’s paper, “Bullshit Universities: The Future of Automated Education,” is deliberately provocative.

Generative AI now helps produce the language through which teaching and learning happen.

ChatGPT can support students’ thinking, but much depends on when it enters the learning process and what students are asked to do with its responses.

A sentence can read more smoothly while becoming less faithful to its source.

A story about a wolf detector, a set of picture cards, and a sorting activity can each open a conversation about AI.

Generative AI tools such as ChatGPT have introduced new possibilities for students to explore ideas, ask questions, and engage with information.

When AI helps write an explanation, design an activity, or draft feedback, what happens to the educator’s role in shaping that work?

Should school AI education prepare every student to build intelligent systems?

AI in education is often presented as a break with everything that came before.

What should students learn about AI when the goal is to understand the technology behind its applications?

When a student produces a stronger piece of work with AI, what have we learned about their learning?

Moving an assessment into an exam hall can feel reassuring when students have access to generative AI.

Teachers are increasingly expected to integrate AI into their work, but how much research examines the professional learning needed to support that expectation?

Every AI tool sells the same promise: less effort, fewer steps, faster to done.

When 70 instructional designers were asked how much they value generative AI for their work, they were enthusiastic.

I’ve argued for a while that the “can a machine grade as well as a teacher?”

I advocate for AI literacy as a core academic skill, and I’ve made that case on this blog more times than I can count.

Collaborative problem solving can improve students’ critical thinking, but simply placing learners in groups is unlikely to produce the same result.

AI in academic publishing can reduce some of the language barriers faced by scholars who write in English as an additional language.

AI professional development for teachers needs to make room for both confidence and concern.

Critical AI literacy asks students to examine how AI shapes knowledge, participation and power.

Large language models do more than help academics and students complete tasks.

Postdigital literacy asks us to stop treating literacy as a collection of separate competencies that can be added whenever a new technology appears.

What is the right age for children to use AI?

Imagine writing an essay entirely on your own and then being accused of using AI because your English is considered too predictable.

AI dialect bias can shape a model’s judgement even when race is never mentioned.

AI in teacher education can support professional learning when the assignment keeps preservice teachers responsible for evaluating and improving what the technology produces.

We often discuss whether people trust AI, resist it or feel threatened by it.

AI overreliance occurs when people accept an incorrect recommendation from an AI system.

AI in education should begin with a question that is often pushed aside: what kind of education do we want?

The postdigital turn in education begins with a simple recognition: digital technology is no longer a separate layer that we occasionally add to teaching.

Postdigital education does not describe a world that has moved beyond digital technology.

Political bias in large language models is difficult to measure because there is no neutral answer key for political questions.

Resisting AI in education begins with a question that is often pushed aside by discussions of tools, policies, and assessment: What do we believe education should be about?

AI discourse deserves the same scrutiny we give AI-generated content.

Trust in AI is often reduced to a simple question: Do people trust the system or not?

Postdigital research is difficult to define by design.

Entangled pedagogy offers a useful correction to one of the most familiar pieces of advice in educational technology: “pedagogy first.”

AI-assisted writing and authorship are often discussed through the language of academic integrity: Did the student write the paper?

Identity in educational research can help us examine how people become recognized as particular kinds of learners, teachers, or professionals.

Critical educational technology asks us to examine how digital tools become part of education: who shapes their use, whose interests they serve, and what happens in everyday classrooms.

AI and peer feedback can work together in a structured writing course.

AI adoption and productivity are positively associated in the cross-country analysis reported by Bendo and colleagues (2026).

AI curriculum adaptation can help teachers explore different explanations, additional activities, and changes to existing materials.

The relationship between AI and creativity may depend partly on what learners are trying to achieve.

The hidden labour behind AI belongs in our discussions of responsible technology use.

Student AI use can coexist with uncertainty about whether that use is appropriate.

Writing as learning means that putting an idea into words can change the idea itself.

Project-based learning gives students opportunities to plan, investigate, collaborate, and reconsider their decisions over time.