Explainable AI in Education and Human-AI Collaboration: New Frameworks for Interactive Intelligence
Two peer-reviewed AI research papers present approaches to improving human-AI interaction: one develops an explainable AI classroom assistant that provides instructor-verified feedback to programming students, while the other proposes a theoretical framework treating interaction itself as the primary unit of analysis for understanding AI intelligence and co-creation. Both works address concerns about AI reliability and explainability by centering human collaboration and interaction dynamics. These contributions matter because they offer practical and theoretical solutions to deploying trustworthy AI systems in educational and collaborative contexts.
The first paper presents an AI-driven classroom assistant designed for introductory programming education that uses explainable AI to analyze student code, identify logical errors mapped to instructor-identified misconceptions, and deliver instructor-authored feedback. The system was evaluated through expert review and classroom deployment, with results showing it can provide accurate, instructor-verified feedback while maintaining positive student experience. The second paper proposes a broader theoretical framework called Interaction-Centered Intelligence, arguing that intelligence and creativity emerge through interaction dynamics among agents, environments, and socio-technical systems rather than through isolated computation alone. Drawing from distributed cognition, embodied cognition, and human-computer interaction research, it reframes how AI systems should be evaluated—emphasizing interaction trajectories and participatory engagement rather than solely output quality. Together, these works address growing concerns about AI explainability and reliability by grounding AI systems in human expertise, collaboration, and interactive processes.
What's missing
Neither paper provides information about scalability beyond their specific implementations, potential limitations when instructor oversight is unavailable, or comparative performance against other feedback systems (such as traditional LLM-based approaches without instructor grounding). The theoretical framework paper does not discuss computational or practical constraints for implementing interaction-centered approaches in real-world systems.
What different sources said
- arXiv cs.AICenter
Examining the Usage of Generative AI Models in Student Learning Activities for Software Programming
- arXiv cs.AICenter
Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation
- arXiv cs.AICenter
An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
- arXiv cs.AICenter
AI-Automation Tooling in Computer Engineering Education: Mixed-Methods TAM/UTAUT Evidence for a General Acceptance Attitude
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