The paper titled "With a Thermomix You Lose the Ability to Cook: A Kitchen Machine Analogy for Applications of Generative AI in Education" uses the Thermomix kitchen appliance as an analogy to explore how generative AI tools like ChatGPT are adopted in education. By mapping Thermomix use cases onto learning scenarios and situating them within the ICAP and SAMR frameworks, the authors illustrate how different modes of tool use can either support or undermine meaningful engagement and learning. The study emphasizes that the key issue is not merely whether learners use AI, but how such use shapes their learning processes, offering a conceptual lens for researchers and practitioners to critically examine and guide AI integration in education.
arXiv:2608.21391v1 Announce Type: cross Abstract: In this research-to-practice paper we present a survey that can be used to assess students' AI knowledge. As the use of artificial intelligence (AI),...
arXiv:2509. 13570v2 Announce Type: replace Abstract: With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important.
arXiv:2606. 12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings.
arXiv:2606. 15601v1 Announce Type: cross Abstract: We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition.
arXiv:2609.07715v1 Announce Type: cross Abstract: The hype around generative AI seems to promise unprecedented productivity (and learning) gains. However, these technologies' increasing agentic featu...
The paper introduces ICE-T, a didactic framework designed to improve trust in AI systems through education. It combines intermodal transfer, computational thinking, and explanatory thinking to address the opacity of machine learning tools. By linking these facets to research on algorithm aversion and AI literacy, the authors argue that trust calibration should be an explicit educational goal.
The paper identifies two main challenges in machine learning education: opaque tools that give learners only a superficial grasp of ML and hinder the development of calibrated trust in AI systems. It introduces ICE‑T, a didactic framework that combines intermodal transfer, computational thinking, and explanatory thinking to provide representational richness, graduated process control, and error contextualization. By linking these facets to trust‑calibration literature, the authors argue that trust should be an explicit educational goal and that ICE‑T offers a scalable method to achieve it.
arXiv:2606. 14306v1 Announce Type: cross Abstract: Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability.
arXiv:2606. 12086v1 Announce Type: new Abstract: Contextualized assessment offers high ecological validity for evaluating creativity but introduces a critical challenge: observed performance may be confounded with cognitive proficiency (domain knowledge) and agency (willingness to engage).
arXiv:2608. 12325v1 Announce Type: new Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today.
arXiv:2609.14638v1 Announce Type: cross Abstract: This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Pr...
arXiv:2608. 03524v1 Announce Type: new Abstract: AGENTONOMICS is a framework that treats AI agents as economic entities that can be designed, managed, and governed through an integrated management architecture.