AINeutralarXiv – CS AI · May 96/10
🧠Prober.ai is an LLM-powered web-based writing environment that uses constrained AI personas and gated feedback mechanisms to improve argumentative writing through inquiry-based questioning rather than text generation. The system addresses cognitive outsourcing in education by forcing student reflection before revealing revision suggestions, grounded in Toulmin's argumentation theory and peer feedback research.
🧠 Gemini
AINeutralarXiv – CS AI · May 96/10
🧠A university study of 35 students examined whether writing counterarguments to AI-generated content develops critical thinking skills. Researchers found that student-written counterarguments demonstrated logical reasoning and that six frontier large language models could reliably assess student work using established rubrics, achieving moderate inter-rater reliability (0.33 Gwets AC2) comparable to human assessments.
AIBullisharXiv – CS AI · May 96/10
🧠Researchers propose leveraging generative AI's errors and hallucinations as pedagogical tools in higher education, specifically within a database design course. By framing AI as an imperfect learning companion, the study demonstrates how structured interaction with AI-generated mistakes can develop students' critical thinking skills and higher-order cognitive abilities aligned with Bloom's taxonomy.
AINeutralarXiv – CS AI · May 16/10
🧠A research framework addresses the challenge of integrating autonomous agentic AI systems into education by balancing three core tensions: implementation feasibility, adaptation speed, and mission alignment. The article argues that educational institutions must proactively manage the gap between rapidly evolving AI capabilities and the institutional capacity to deploy them responsibly while maintaining pedagogical integrity.
AINeutralarXiv – CS AI · Apr 146/10
🧠George Mason University's UNIV 182 course demonstrates that AI literacy education can achieve both technical depth and broad accessibility without prerequisites. The course uses a five-part pedagogical framework including structured problem-solving pipelines, ethics integration, peer critique sessions, cumulative portfolios, and AI tutoring agents to guide non-technical undergraduates from conceptual understanding to building functional AI systems.
AIBullisharXiv – CS AI · Apr 74/10
🧠Researchers developed CODE-GEN, a human-in-the-loop AI system that uses retrieval-augmented generation to create multiple-choice programming questions for educational purposes. The system achieved 79.9% to 98.6% success rates across seven pedagogical dimensions when evaluated by subject-matter experts, demonstrating strong performance in computational verification tasks while still requiring human expertise for complex instructional design.
AINeutralarXiv – CS AI · Apr 65/10
🧠Researchers compared custom pedagogy-informed AI chatbots with general-purpose chatbots like ChatGPT for science education, finding that custom chatbots using Socratic questioning methods increased student cognitive engagement and reduced cognitive offloading. The study analyzed 3,297 student-chatbot dialogues from 48 secondary school students, showing higher interaction intensity with custom chatbots despite similar problem-solving performance outcomes.
🧠 ChatGPT
AINeutralarXiv – CS AI · Mar 94/10
🧠Researchers propose a unified instructional framework that integrates MOOCs, Smart Teaching, and AI-enhanced learning to address limitations of isolated adoption in higher education. The framework organizes these technologies into three complementary dimensions: structured exposure, adaptive allocation, and efficiency amplification to maximize pedagogical effectiveness.
AINeutralarXiv – CS AI · Mar 54/10
🧠Researchers developed a web tool that uses natural language as the primary interface for LLM-assisted educational game design, allowing instructors to collaborate with AI to create games with specific learning outcomes. The tool maps pedagogy to gameplay through four linked components while maintaining human agency in critical design decisions.
AINeutralarXiv – CS AI · Mar 54/10
🧠A study of 29 STEM faculty members reveals mixed adoption of generative AI tools in higher education, with educators using AI for content generation and curriculum design while expressing concerns about academic integrity and assessment validity. The research highlights the need for institutional support and rethinking of pedagogical approaches to effectively integrate AI technologies into educational settings.
AINeutralarXiv – CS AI · Mar 34/106
🧠Researchers analyzed how university STEM instructors customize AI chatbots for classroom use, identifying ten common categories of customization. The study found that instructors prioritize aligning chatbot behavior with course materials over persona customization, but needs vary significantly by course size and teaching style, suggesting modular AI chatbot designs would be most effective.
