Abstract
The impact of Generative AI (GenAI) is reshaping how we assess our students and how they write their assessments. As a transformational technology, the challenge lies not only in integrating GenAI tools into our teaching but doing so in ways that foster critical engagement rather than passive adoption, ensuring that we take a balanced approach to how they are used.
The University of Manchester’s credit bearing course, the Digital Society sits within the University College for Interdisciplinary Learning and is taught entirely online. The course is owned by the library; it is convened, managed, designed and assessed by two members of Library staff and the wider library teaching team. In this presentation we will focus on how GenAI has been introduced into the Digital Society, to enhance students’ information literacy, digital literacy and critical thinking (Francis, Jones and Smith, 2024).
GenAI cannot be viewed as a peripheral technology; its impact on academic workflows and student behaviour has been profound (Pallant et al., 2025).
As teachers this raises two important points:
1. Authentic Integration: GenAI should not be an add-on but embedded meaningfully across the curriculum (Smith, Sokoya and Moore, 2025).
2. Critical Engagement: Students must learn to apply their critical lens to GenAI outputs just as they would to their standard academic practice; evaluating outputs, understanding bias and limitations, and applying ethical reasoning when using these tools.
We used three principles to integrate GenAI into the unit:
1. Distributed integration across topic weeks
Rather than isolating GenAI in one topic, its use is threaded through weekly themes e.g. Critical analysis in a digital world, AI ethics and us, The Internet etc.
2. Scaffolded activities building criticality
In the critical analysis topic week the students are asked to use a GenAI tool to write their first piece of assessment and then critically analyse the results, posting their reflections in a shared space. This scaffolding ensured the use of GenAI tools was linked to the students critical reflective practice (Belkina et al., 2025).
3. GenAI disclosure
Assessment requires students to document their engagement with GenAI, including prompts used, outputs generated, and critical reflections. This transparency promotes accountability and positions GenAI as a partner in learning rather than a shortcut.
Challenges and opportunities
Introducing GenAI into credit-bearing courses raises concerns around academic integrity, equity of access, and staff readiness. Addressing these requires:
• GenAI itself: It is in a constant state of flux – how do we build this in when it’s changing from day to day.
• Students and GenAI: Why, how and what are they using?
The process of scaffolding GenAI throughout a credit bearing unit has been an interesting development and during the session attendees will gain an insight into our approach as we reflect on the process itself, how it has developed and how GenAI can potentially be used to move students towards a state of deeper learning around using GenAI tools and the authentic unit assessments (Pallant et al., 2025).
References
Belkina, M., et al. (2025). ‘Implementing generative AI (GenAI) in higher education: A systematic review of case studies’ Computers and Education: Artificial Intelligence, 8 p. 100407 https://doi.org/https://doi.org/10.1016/j.caeai.2025.100407.
Francis, N. J., Jones, S. and Smith, D. P. (2024). ‘Generative AI in Higher Education: Balancing Innovation and Integrity’ British Journal of Biomedical Science, Volume 81 - 2024 https://doi.org/10.3389/bjbs.2024.14048.
Pallant, J. L., et al. (2025). ‘Mastering knowledge: the impact of generative AI on student learning outcomes’ Studies in Higher Education, pp. 1-22 https://doi.org/10.1080/03075079.2025.2487570.
Smith, D. P., Sokoya, D. and Moore, S. (2025). ‘Embedding Generative AI as a digital capability into a year-long skills program’ Journal of University Teaching and Learning Practice, 22 (4), https://doi.org/10.53761/fh6q4v89.
