The rapid adoption of Generative AI (GenAI) tools by students created uncertainty around academic integrity, assessment design, and the future of teaching. Traditional assessments risked being gamed by AI, while educators lacked evidence\u2011based guidance on integrating these tools in ways that support learning rather than undermine it.<\/p>\n
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Our solution (approach)<\/span><\/strong><\/h3>\nFraming the problem:<\/strong>
\nTraditional assessments often measure static product outcomes rather than student process, analysis, and judgement. With GenAI, the ability of students to generate text and code autonomously required a shift towards assessments that foreground critical thinking, metacognition, and process evaluation.<\/p>\nHow we responded:<\/strong><\/p>\n\n- Designed and facilitated an inclusive cross\u2011institutional symposium to gather diverse perspectives on AI and assessment.<\/li>\n
- Developed practitioner resources drawing from the symposium outcomes \u2014 including examples of AI\u2011integrated assessment tasks that emphasise reasoning, critique, and iteration.<\/li>\n
- Shared evidence\u2011informed practice with faculty colleagues and contributed to wider sector discussions through academic networks.<\/li>\n<\/ul>\n
Tools & methods:<\/strong><\/p>\n\n- Workshop facilitation with breakout activities to prototype AI\u2011aware assessment tasks<\/li>\n
- Case examples of AI co\u2011creation exercises in coding and writing<\/li>\n
- Critical reflection tasks embedded within assessment rubrics<\/li>\n<\/ul>\n
\nThe impact (results)<\/span><\/strong><\/h3>\nOn students:<\/strong>
\nIn my modules, students have responded positively to the opportunity to engage with AI tools as learning partners rather than threats. They report increased confidence in evaluating digital outputs, improved understanding of authorship and ethics, and a clearer sense of how to navigate digital workspaces in future employment contexts.<\/p>\nOn colleagues and practice:<\/strong>
\nI have shared my approach through informal workshops and teaching networks. While adoption is still early-stage, colleagues have expressed interest in adapting these assessment models to their own disciplines, particularly for scaffolded reflective assessment.<\/p>\nOn scholarly engagement:<\/strong>
\nThis work has led to a peer-reviewed manuscript and has supported further collaborations around AI literacy and assessment design. I am integrating these insights into future curriculum development.<\/p>\n
\nLessons learned<\/span><\/strong><\/h3>\nThis initiative deepened my appreciation for assessment as a dynamic conversation rather than a static measure. Designing assessments for the age of AI is as much about what we value, critical thinking, resilience, ethical judgement, as it is about technology. Next steps include scaling AI\u2011aware assessment examples into the new curricula, and continuing practitioner research on assessing digital fluency.<\/p>\n
\nTips for colleagues<\/span><\/strong><\/h3>\n\n- AI requires a shift from product\u2011centric to process\u2011centric assessment.<\/li>\n
- Collaborative, cross\u2011disciplinary dialogue accelerates innovation in educational design.<\/li>\n
- Pedagogy should foreground learner agency, reflection, and ethical engagement with technology.<\/li>\n<\/ul>\n
\nSkills and attributes<\/span><\/strong><\/h3>\nStudents were able to develop the following attributes:<\/p>\n
\n- Digital skills<\/a><\/li>\n
- Collaboration<\/a><\/li>\n
- Leading and influencing<\/a><\/li>\n
- Future focused<\/a><\/li>\n<\/ul>\n
\nEducation for Life Strategy<\/span><\/strong><\/h3>\n
\nTraditional assessments often measure static product outcomes rather than student process, analysis, and judgement. With GenAI, the ability of students to generate text and code autonomously required a shift towards assessments that foreground critical thinking, metacognition, and process evaluation.<\/p>\n
How we responded:<\/strong><\/p>\n Tools & methods:<\/strong><\/p>\n On students:<\/strong> On colleagues and practice:<\/strong> On scholarly engagement:<\/strong> This initiative deepened my appreciation for assessment as a dynamic conversation rather than a static measure. Designing assessments for the age of AI is as much about what we value, critical thinking, resilience, ethical judgement, as it is about technology. Next steps include scaling AI\u2011aware assessment examples into the new curricula, and continuing practitioner research on assessing digital fluency.<\/p>\n Students were able to develop the following attributes:<\/p>\n\n
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\nThe impact (results)<\/span><\/strong><\/h3>\n
\nIn my modules, students have responded positively to the opportunity to engage with AI tools as learning partners rather than threats. They report increased confidence in evaluating digital outputs, improved understanding of authorship and ethics, and a clearer sense of how to navigate digital workspaces in future employment contexts.<\/p>\n
\nI have shared my approach through informal workshops and teaching networks. While adoption is still early-stage, colleagues have expressed interest in adapting these assessment models to their own disciplines, particularly for scaffolded reflective assessment.<\/p>\n
\nThis work has led to a peer-reviewed manuscript and has supported further collaborations around AI literacy and assessment design. I am integrating these insights into future curriculum development.<\/p>\n
\nLessons learned<\/span><\/strong><\/h3>\n
\nTips for colleagues<\/span><\/strong><\/h3>\n
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\nSkills and attributes<\/span><\/strong><\/h3>\n
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\nEducation for Life Strategy<\/span><\/strong><\/h3>\n
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