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