Med Kharbach

AI and teacher workload have become closely connected in discussions about educational technology.

Generative AI in STEAM education can contribute explanations, design ideas, code, and media to classroom projects.

Responsible AI use in mathematics requires students to judge whether a generated explanation is valid and whether they understand the reasoning behind it.

AI writing revision can change how an argument positions its claims and responds to other viewpoints.

Generative AI and critical thinking can come together in classroom activities that require students to examine, correct, and explain AI-generated material.

Teachers’ AI cognitive offloading happens when we hand some of our planning, explanation, feedback, or assessment work to an AI system.

AI writing and authorial voice can feel like uneasy companions.

High-achieving students and AI are often discussed as though strong students will naturally find the best ways to use a powerful tool.

Generative AI use in higher education is often discussed as though lecturers can see what students are doing with these tools.

AI scaffolding and student motivation do not necessarily move together over time.

AI-associated words in academic abstracts have become much more common since 2022.

GenAI-assisted language teaching can easily become a collection of prompts and classroom activities.

Agentic AI and collective intelligence may develop through networks of humans and AI systems, according to a new essay by James Evans, Benjamin Bratton, and Blaise Agüera y Arcas.

AI feedback in higher education can give students timely, accessible guidance, but its effects do not stop with the exchange between a learner and a chatbot.

AI detector false positives can turn authentic student writing into apparent evidence of misconduct.

The connection between open-ended questions and creativity appears to be more than a familiar teaching assumption.

Can researchers trust LLM data extraction for systematic reviews?

Interactive research papers could make a study’s methods usable through ordinary language instead of leaving readers to install software, interpret code, and reconstruct analytical workflows for themselves.

I read something like forty student emails a week.

AI writing instruction often begins with a broad permission question: should students be allowed to use these tools?

Do large language models have emotions?

ChatGPT and student creativity can work together, but access to AI generated ideas does not necessarily lead students into deeper creative thinking.

ChatGPT and creative writing produced a striking split in a 2025 experiment by Peidong Mei and colleagues.

Generative AI and academic skills are often discussed in extremes.

Fabricated AI citations can survive several layers of scholarly review when authors, editors, and reviewers assume that a plausible looking reference must be real.

An AI literacy curriculum can give teachers and students a clearer understanding of how AI works, where it is used, and what ethical questions it raises.

AI can help educators generate and refine ideas for learning activities, but it cannot take responsibility for the educational design.

Language learners need access to spoken language, but access is only part of the story.

AI and academic achievement were positively related across a 2025 meta-analysis by Liu Dong, Xiuxiu Tang, and Xiyu Wang.

Language prestige is created through social practice.

AI writing assignment design needs to address a question that simple permission statements often leave unresolved: what work should students do themselves, and where can AI make a useful contribution?

AI detection in academic writing can create a problem that receives far less attention: researchers may rewrite legitimate work simply to satisfy an algorithm.

Critical consciousness is closely associated with the work of Brazilian educator Paulo Freire.

AI detector accuracy may be considerably better than earlier evaluations suggest, at least for some current commercial systems and under controlled test conditions.

Socioaffective alignment is a way of evaluating AI that takes account of what happens to people through an ongoing social and emotional relationship with a system.

Can AI companions reduce loneliness?

AI chatbots and adolescents can form a powerful combination because a few seemingly ordinary language choices can make a system feel more human, trustworthy, and emotionally close.

AI can make people feel emotionally closer to a conversational partner, but that does not mean the system understands or shares their feelings.

The headline finding in this study is difficult to ignore: in a Harvard physics course, students learned more from an AI tutor than from an in-class active-learning lesson.

When a technology becomes useful enough, opting out can become difficult.

Technical and vocational education faces a particularly demanding version of the AI question.

Ethical AI use in higher education involves more than following a rule about whether ChatGPT is permitted.

Universities cannot fully reform assessment for generative AI while leaving grades outside the discussion.

Generative AI in multilingual classrooms can help students translate, compare expressions, and draw on their strongest languages.

Artificial intelligence basics are often explained through a parade of tools: chatbots, image generators, recommendation engines, and autonomous systems.

“Stochastic parrots” has become one of the most recognizable phrases in debates about large language models.

Trust in AI answers is often built from the language we see on the screen.

What is AI literacy?

Human-AI collaboration is often described through one of two extremes.

The future of AI in education is regularly presented as though the important decisions have already been made.