{"id":3103,"date":"2025-10-17T12:36:20","date_gmt":"2025-10-17T11:36:20","guid":{"rendered":"https:\/\/blogs.manchester.ac.uk\/itl\/?p=3103"},"modified":"2026-04-09T09:48:52","modified_gmt":"2026-04-09T08:48:52","slug":"responsible-ai-in-learning-a-student-informed-approach","status":"publish","type":"post","link":"https:\/\/blogs.manchester.ac.uk\/itl\/2025\/10\/17\/responsible-ai-in-learning-a-student-informed-approach\/","title":{"rendered":"Responsible AI in Learning: A Student-Informed Approach"},"content":{"rendered":"

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Dr Wennie Subramonian and Dr Maryam Malekshahian are Senior Lecturers (Teaching and Scholarship) in the Department of Chemical Engineering. Wennie also contributes as the Department\u2019s Deputy Admissions and Outreach Lead. Her work focuses on enhancing inclusive student learning through the responsible use of AI in both classroom and lab settings, as well as in outreach projects. She leads student partnership projects that embed student voice in shaping inclusive, effective teaching and learning practices.<\/p>\n

Maryam is the department\u2019s Equality, Diversity, Inclusion, and Accessibility (EDIA) Lead and former Academic Employability Lead. Her current work explores how AI can be used thoughtfully and equitably in shaping an inclusive engineering education.<\/p>\n

At the recent UoM Teaching and Learning Conference 2025, they invited colleagues to consider how students can move from passive users to co-creators of responsible AI practices.<\/p>\n

What motivates us to explore this area?<\/h5>\n

Our work is driven by a notable gap in ethical guidance for students using AI in higher education. To date, only one ethical framework has been identified for higher education and it is primarily tailored to educators, not students. Developed by the Asian University for Women and the International University of Bangladesh, this framework does not sufficiently address the needs or perspectives of students as AI users. Moreover, there is limited evaluation of how AI-supported learning activities influence student learning outcomes in Engineering. In the past two years, only six such studies have been published, highlighting the need for further exploration in this area.<\/p>\n

Our departmental student voice also revealed key concerns. In a recent classroom study, 94%<\/strong> of Chemical Engineering students (n=35) expressed worry about becoming over-reliant on AI tools, highlighting the importance of equipping students with the skills to use AI critically and responsibly.<\/p>\n

\"AFigure 1<\/strong>: Main concerns of students using AI (n=35).<\/em><\/p>\n

Student Co-Creation in Action<\/h5>\n

Our project, co-led with MEng student Mr Ahnaf Saumik, began with a core question: How can students be supported to use AI critically and responsibly in their learning?<\/em>
Together, we co-developed a concise two-page guide and classroom learning activities focused on responsible AI use, addressing academic integrity, data privacy, and bias. One key activity involved the Bhopal disaster as a Year 3 Chemical Engineering case-based learning, where students used AI tools to support their reflection on engineering ethics, safety, and problem-solving.<\/p>\n

This partnership placed students not only at the centre of the design but at the centre of the conversation, ensuring the language, concerns, and practices in the resources were truly student-relevant.<\/p>\n

What our students say about the AI Responsible Guide and Learning Activity<\/h5>\n

Over 91%<\/strong> of students found the Responsible AI learning activity useful, with 62% rating it as very to extremely useful (Figure 2<\/strong>). Students particularly valued the ethical guidance provided, commenting that \u201cthe ethical points were helpful,\u201d and praised the resources as \u201creally good and concise.\u201d Some students suggested future improvements, such as incorporating \u201cmore techniques\u201d to support AI use in learning.<\/p>\n

However, only 32% of students reported feeling confident in the AI-generated responses when completing the learning activity (Figure 3<\/strong>). This highlights a continued need for academic support in helping students critically evaluate and apply AI outputs, particularly in problem-solving contexts.<\/p>\n

\"AFigure 2<\/strong>: Effectiveness of the learning activity in applying the guides information (n=35).<\/em><\/p>\n

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\"A<\/p>\n

Figure 3<\/strong>: Students rank their confidence in the accuracy of AI outputs for the learning activity (n=35).<\/em><\/p>\n

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Wicked Problem Set Workshop: Moving from Tools to Staff reflection<\/h5>\n

In our workshop, participants consisting of academics and professional service staff explored the student-designed guide and learning activity, and reflected on its relevance for teaching. Instead of completing the learning activity, they focused on what AI raises in terms of assumptions, risks, and opportunities in ethically complex tasks. We then moved into a wider discussion: How can educators foster critical, student-informed AI use across disciplines? What support do staff need to confidently and responsibly embed AI into their practice?<\/p>\n

Key Insights from the Room<\/h5>\n

We invited participants to complete a short survey to help shape future support initiatives. Some highlights:<\/p>\n