Reimagining Assessment in an AI-Rich Enviroment: A Learning Exchange

This free event is supported by the DMU Education Academy and aims to foster an inclusive, supportive environment for educators across disciplines, career stages, and roles. As noted by Advance HE and the QAA, cross-institutional collaboration is essential for developing resilient and future-facing assessment practices.

Join us to help build that future—one assignment at a time.

This learning exchange will aim to:

• Empower educators to create assessment strategies that uphold deep, critical learning—even in an AI-rich environments

• Curate of a toolkit of assessment designs, rubrics, AI-use guidelines, and evaluation resources.

• Inform local and institutional policy through grounded, practice-led insights.

• Support the development of community of practice on AI and assessment. · Explore interest in a special issue of Studies in Empowering Education.

The emergence of Generative Artificial Intelligence (henceforth AI) caught many sectors, including Higher Education, ‘off guard’ (Sullivan et al 2023). The disruptive but also transformative potential of these technologies and their widespread adoption (HEPI, 2024; Malström et al., 2023) saw concerns raised about the risks they posed to academic integrity, the credibility of assessment processes, and raised questions of authorship and originality and the potential negative impact of these tools on meaningful deep learning (Fischer et al 2024; Moorehouse et al 2023; Luo 2024). Such concerns saw the use of AI tools initially banned by some HEIs whereas other institutions developed guidelines for use, in some cases requiring that AI use was declared and acknowledged (Moorehouse et al 2023).

Following the long-standing use of tools like Turnitin to detect academic misconduct, attention then turned to monitoring student use of AI through the deployment of AI-detection tools. However, there is now almost consensus that AI detectors are unreliable (Chaka, 2024; Hutson 2024). Furthermore, some research has suggested that false positives from such detectors disproportionately affect neurodivergent writers and those for whom English is an additional language, making their use potentially unethical (Gregg-Harrison, 2024). Further concern has since emerged amongst the higher education community that AI is also causing an ‘erosion of trust’ between students and educators (Ple, 2023) with a perceived lack of two-way transparency about how these tools are used (Luo, 2025).

Bodies such as Advance HE and the Quality Assurance Agency for Higher Education have emphasised the need for agility in HEIs to respond to the threats but also immense opportunities offered by an AI-rich environment. Indeed, the emergence of AI has precipitated a shift to consider different methods of assessment and to be more reflexive and activity consider: what is assessment for? Educators are now therefore tasked with rethinking methods of assessment to ensure that core skills of critical thinking, synthesis and analysis are developed and maintained. Some argue that universities should adopt more authentic assessments that connect learning to real-world contexts, as these are often positioned as a means of fostering AI resilience in assessment design. However, research suggests such tasks may still be susceptible to AI use, and this approach may not offer the panacea some have claimed (Kofinas et al., 2025).

A further goal may be for educators to leverage AI as a tool to enhance the educational experience, including the design and completion of academic assessments, rather than to replace the critical thinking and creative processes central to learning (Luo 2024). By doing so, it is hoped educators can foster an environment in which technology augments—rather than undermines—academic integrity and the pursuit of knowledge (Hutson, 2024). This may therefore include the development of assessments that explicitly require or encourage the use of AI tools during assessment preparation, reflecting the view that AI literacy, if not fluency, is likely to become a critical employability skill for students in the near future (Pagini et al., 2023). Yet the integration of generative AI into higher education too raises critical concerns about the risk of deepening existing inequalities, concerns about data privacy, algorithmic bias, and the environmental impact.

It is clear that as AI reshapes the landscape of higher education, academic assessment stands at a critical juncture. This teaching and learning exchange seeks to bring together educators, learning designers, librarians, and teaching-focused staff in all discipline areas to explore how assessment practices are evolving in response to AI and other transformative pressures.