CONF.i: A Confidence-Informed AI Feedback Framework for Canvas LMS in Engineering Education

The increasing adoption of generative artificial intelligence (AI) in higher education has created new opportunities to enhance Learning Management Systems (LMS) with personalized feedback, adaptive assessment, and learning analytics. Despite these advances, many LMS platforms remain primarily focused on content delivery and grade management, with limited support for metacognitive assessment and intelligent feedback. This study presents CONF.i, a confidence-informed assessment and AI feedback framework integrated with Canvas LMS using Google Apps Script and Google Gemini AI. Developed through a design-based research approach, the framework combines traditional assessment scores with student self-reported confidence levels to support personalized formative feedback and diagnostic learning insights. The proposed system integrates Canvas LTI standards, a Google Apps Script backend, and Gemini AI services to automate scoring, confidence tracking, and AI-generated educational feedback within existing institutional infrastructure. A prototype implementation was evaluated using simulated learner profiles representing different combinations of performance and confidence patterns. The framework identified four illustrative assessment profiles: aligned mastery, underconfident competence, overconfident struggle, and aligned struggle. These patterns demonstrate how confidence-informed assessment can reveal metacognitive dimensions of learning that are not visible through conventional grading alone. Preliminary usability observations indicated positive perceptions regarding the integration within the familiar Canvas environment and the relevance of AI-generated feedback, while also identifying limitations related to response latency and feedback specificity. The findings suggest that integrating confidence-informed assessment with generative AI may support more personalized and reflective learning experiences without requiring major institutional infrastructure changes or commercial licensing costs. This study contributes an exploratory prototype framework for AI-enhanced formative assessment in higher education and provides a practical model for institutions seeking to extend existing LMS platforms with confidence-aware analytics and personalized feedback capabilities.