A Guided Ethics Assessment for Early Prototypes in AI Education - Krishna, Abhiram Radha; Josekutty, Jerardh; Vilasan, Adhish Anitha; Gokul, Krishnajith; Jayachandran, Abhijith Jyothi; Johnson, Joshwin Emmanuel; Saleem Kallan, Muhammed; Sajeev, Sanjay (HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice, 2026)
Students and first-time developers building AI prototypes often struggle to translate high-level ethical principles into concrete actions relevant to their specific projects. This paper introduces a Guided AI Ethics Assessment, an interactive educational tool designed to bridge this gap. Through a guided questionnaire enriched with contextual hints, real-world case studies, and analogies, the tool helps students systematically analyze the purpose, data sources, and capabilities of their project. A key objective of this process is to ensure that high-risk and critical considerations, such as those outlined in the EU AI Act, are not overlooked. Based on their responses, the system employs a "rules-as-data"architecture to deterministically generate two key educational artifacts: a prioritized Risk Assessment Report that explains the potential issues identified during the self-assessment, and a transparent Decision Trace that shows the student how their design choices led to these specific recommendations.By transforming abstract principles into a structured, reflective dialogue, our tool makes responsible AI development a tangible, reviewable, and teachable component of AI education. We present the tool's pedagogical design, its rigorous evaluation against a diverse set of synthetic use cases, and a workflow for classroom integration, offering a practical approach to embedding ethical deliberation into the training of the next generation of AI builders.
CRIS-Team Scopus Importer:2026-03-13
Keywords: AI ethics; checklists; decision tables; education; governance; reproducibility; rules-as-data