The study explores how children design AI chatbots and what they consider trustworthy. Using a custom chatbot-building environment, 115 learners aged 8‑18 created 119 chatbots and adjusted traits such as confidence, transparency, and formality. Findings show younger children equate trust with purpose‑fulfillment, while older children focus on transparent, calibrated design, and that students calibrate academic chatbots to be more formal and transparent than hobby ones.
By Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Catet\'e, Tiffany Barnes, Xiaoyi Tian
The study examined how different designs of AI teaching assistants (AI TAs) affect students in an introductory programming course. Four AI TAs were compared based on pedagogical style (Socratic vs. Direct instruction) and context awareness (no context vs. full context). Results showed that the Socratic AI TA with full context received the lowest favorability ratings, had the highest interaction stress, the most external LLM use, and the lowest comprehension outcomes, though differences were not statistically significant.
By Madeleine Eastwood, Harshith Narne, Joseph Hilby, Paul Denny, Ashish Aggarwal, Amanpreet Kapoor
arXiv:2607. 24755v1 Announce Type: cross Abstract: This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses.
By Marina Lepp
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.
By Pierre Haritz, Hendrik Krone, Thomas Liebig
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:2607. 00211v1 Announce Type: new Abstract: Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies.
By Mengqian Wu
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),...
By Aditya Johri, Cory Brozina, Akriti Bagale
arXiv:2511. 13271v2 Announce Type: replace-cross Abstract: The rise of Generative AI (GenAI) tools like ChatGPT has created new opportunities and challenges for computing education.
By Rufeng Chen, Shuaishuai Jiang, Jiyun Shen, AJung Moon, Lili Wei
Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively. Generative AI offers a credible means of addressing the provision challenge, but students' uptake of AI-generated feedback remains limited.
arXiv:2608. 11625v1 Announce Type: new Abstract: Feedback processes strongly influence student learning, yet their educational value depends on addressing two distinct challenges: providing high-quality, timely, and individualised feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively.
By Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge, Dragan Ga\v{s}evi'c, Naomi Winstone, Hassan Khosravi
arXiv:2606. 20605v2 Announce Type: replace-cross Abstract: Background: Generative artificial intelligence (GenAI) is increasingly used for health information, yet its influence on users' trust calibration remains unclear.
By Arif Ahmed, Gondy Leroy, Agrim Sachdeva, Philip Harber, Stephen A. Rains, Seokjun Youn, Prosanta Barai
arXiv:2607. 24601v1 Announce Type: cross Abstract: Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to understand.
By Zhenhan Gao, Marvin Mu\~noz Bar\'on, Umm-e Habiba, Daniel Graziotin, Stefan Wagner