Presenter(s): Sampath Jayalath 

AI is rapidly reshaping assessment, tutoring, admissions and classroom analytics, but adoption often outpaces structured ethical design. This two-hour, interactive workshop equips educators with a practical, evidence-informed method to embed ethics into AI-enabled teaching and learning systems using the IEEE 7000-2021 standard (Model Process for Addressing Ethical Concerns During System Design). Participants will translate high-level values such as fairness, transparency, privacy, bias, and accountability into testable requirements for real educational use cases (automated grading, adaptive learning tutors, student-risk early warning and proctoring/monitoring). Through short demos and guided activities, attendees will practice stakeholder elicitation, value-to-requirement mapping, ethical risk assessment and verification planning (e.g. bias audits, explainability checks, data minimisation). By the end of the session, attendees will understand what it means to develop and deploy ethical AI systems in their classrooms and what factors to look out for. Practical take-aways include templates and checklists to support implementation.  

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