Maria KISELEVA

Abstract: Generative AI has entered the everyday life of students and lecturers. It can draft essays, summarise literature, write code, support presentations, map research fields, and assist course design. The question for universities is no longer whether students will use AI, but whether they will learn to use it responsibly. This session explores how higher education institutions can teach responsible AI use as part of academic formation. Drawing on practical classroom experiences from Indonesia, Malaysia, Russia, and Asia-Pacific partner universities, the panel examines the ethical boundary between AI as an academic assistant and AI as a substitute for learning. It argues that AI can support brainstorming, translation, argument structure, literature search, and initial drafting, but it must not replace understanding, verification, intellectual ownership, or oral defense of one’s own work. The session presents concrete pedagogical strategies: disclosure-based AI policies, reflective AI-use statements, prompt documentation, source verification, oral assessment, and redesign of assignments around critical judgment. Participants will discuss how universities can move from prohibition or denial toward transparent, accountable, and human-centered AI integration. The session concludes that the future competitive advantage of universities is not artificial intelligence alone, but human intelligence guided by values. Learning objectives: - Distinguish responsible AI assistance from academic replacement, identifying when AI supports learning and when it undermines understanding, authorship, and intellectual ownership. - Design disclosure-based classroom policies for generative AI, including documentation of tools used, prompts entered, sections assisted by AI, and students’ own verification and modification processes. - Apply assessment strategies that protect human intelligence, including oral defense, source verification, reflective AIuse statements, and assignments centered on judgment, ethics, creativity, and critical thinking. Target audience: This session is designed for faculty members, teaching and learning centres, academic programme directors, quality assurance teams, student support services, academic integrity officers, and university leaders responsible for digital transformation in teaching. Especially relevant for: AI literacy; graduate employability

Abstract: Generative AI has entered the everyday life of students and lecturers. It can draft essays, summarise literature, write code, support presentations, map research fields, and assist course design. The question for universities is no longer whether students will use AI, but whether they will learn to use it responsibly. This session explores how higher education institutions can teach responsible AI use as part of academic formation. Drawing on practical classroom experiences from Indonesia, Malaysia, Russia, and Asia-Pacific partner universities, the panel examines the ethical boundary between AI as an academic assistant and AI as a substitute for learning. It argues that AI can support brainstorming, translation, argument structure, literature search, and initial drafting, but it must not replace understanding, verification, intellectual ownership, or oral defense of one’s own work. The session presents concrete pedagogical strategies: disclosure-based AI policies, reflective AI-use statements, prompt documentation, source verification, oral assessment, and redesign of assignments around critical judgment. Participants will discuss how universities can move from prohibition or denial toward transparent, accountable, and human-centered AI integration. The session concludes that the future competitive advantage of universities is not artificial intelligence alone, but human intelligence guided by values. Focus: classroom practice, academic integrity, student judgment, and responsible AI literacy. Learning objectives: - Distinguish responsible AI assistance from academic replacement, identifying when AI supports learning and when it undermines understanding, authorship, and intellectual ownership. - Design disclosure-based classroom policies for generative AI, including documentation of tools used, prompts entered, sections assisted by AI, and students’ own verification and modification processes. - Apply assessment strategies that protect human intelligence, including oral defense, source verification, reflective AIuse statements, and assignments centered on judgment, ethics, creativity, and critical thinking. Target audience: This session is designed for faculty members, academic programme directors, quality assurance teams, student support services,and university leaders responsible for digital transformation in teaching. It will be especially relevant for: assessment redesign; responsible digital education; human-centred higher education.