This post is co-authored by Simon Ross and Grace Billiris.

In today’s educational landscape, the integration of generative artificial intelligence (GenAI) presents challenges and opportunities. Meet two academics, Adrian Kelly and Jianchun Li, who are at the forefront of this education turning point. They’re crafting innovative assessment methods that empower students to have ethical, effective engagement with GenAI while staying rooted in authentic learning experiences.

Adrian Kelly is the mastermind behind Engineering Professional Practice 1, a subject aimed at shaping future engineers. His goal is clear: guide students on a transformative journey toward self-discovery and professional growth.

Jianchun Li, from the School of Civil and Environmental Engineering at UTS, is a seasoned researcher with expertise in structural health. He seamlessly merges his knowledge with AI, particularly machine learning, to study smart building materials’ resilience against earthquakes. His subject, Structural Dynamics and Earthquake Engineering, imparts this wisdom to postgraduate students.

The challenge for educators like Adrian and Jianchun is twofold: how to harness the potential of GenAI whilst ensuring students understand its limitations. Balancing GenAI’s benefits with nurturing essential self-reflection and self-evaluation skills is no small task.

Authentic assessment with Adrian Kelly

Adrian Kelly’s innovative approach is rooted in authentic assessment. He employs a feedback literacy framework that encourages students to deeply engage in self-reflection and collaborative learning. His assessment tasks require students to bridge theory and practice, fostering deep, contextual learning.

a diagram showing assessment tasks in Adrian's subject, with three groups. each group includes a formative in class task, followed by an assessment task. at the end of this timeline is 'student's professional growth'
Feedback actionable in subsequent tasks (Alisa Percy)

Adrian also stands apart by shifting the focus from marks to holistic learning. He believes in understanding the subject matter rather than chasing grades. His philosophy, “When marked summatively, if students get 50%, they pass. This tells them that it is OK not to know the other 50%,”thus, Adrian encourages students to pursue a more comprehensive understanding of the subject matter.

Thinking ahead with Jianchun Li

Jianchun Li shares Adrian’s passion for addressing the generative AI challenge. He prepares students for a world where AI could impact structural engineering. Jianchun fosters creative engagement and ongoing reflections on effective generative AI usage, deepening engineering judgments. His classroom is an open forum for students to discuss AI use, utilising surveys and online discussions in a collaborative environment.

One unique aspect of Jianchun’s approach is treating GenAI as an additional group member for problem-based learning projects. Students are required to log GenAI’s involvement, allowing them to evaluate its role after project completion.

Adapting to GenAI

Adrian and Jianchun aren’t just educators; they’re learners, embodying a range of academic teaching capabilities. They navigate the generative AI landscape alongside their students, recognising the importance of adaptability and innovation. As education evolves, they will continue to empower students equipping them with the skills needed to navigate an ever-changing technological world.

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