Tag: Academic Identity and Sensemaking

By Kerith George-Briant and Jack Hogan, Abertay University Dundee

O’Mahony’s provocatively titled “Something Wicked This Way Comes” blog outlined feelings we recognised from across the sector, which were that Generative AI (GenAI) tools have created unease, disruption, and uncertainty. In addition, we felt that GenAI provided huge opportunities, and as higher education has led and celebrated innovation in all disciplines over centuries, how this translated into our assessment practices intrigued us. 

At Abertay University, we’ve been exploring the “wicked problem” of whether to change teaching practices through a small-scale research project entitled “Lane Change Ahead: Artificial Intelligence’s Impact on Assessment Practices.” Our findings agree with O’Mahony’s observations that while GenAI does pose a challenge to academic integrity and traditional assessment models, it also offers opportunities for innovation, equity, and deeper learning, but we must respond thoughtfully and acknowledge that there are a variety of views on GenAI.

Academic Sensemaking

To understand colleagues’ perspectives and experiences, we applied Degn’s (2016) concept of academic sensemaking to understand how the colleagues we interviewed felt about GenAI. Findings showed that some assessment designers are decoupling, designing assessments that use GenAI outputs without requiring students to engage with the tools. Others are defiant or defeatist, allowing limited collaboration with GenAI tools but awarding a low percentage of the grade to that output. And some are strategic and optimistic, embracing GenAI as a tool for learning, creativity, and employability.

The responses show the reasons for unease are not just pedagogical; they’re deeply personal. GenAI challenges academic identity. Recognising this emotional response is essential to supporting staff if change is needed.

Detection and the Blurred Line

And change is needed, we would argue. Back in 2023, Perkin et al’s analysis of Turnitin’s AI detection capabilities revealed that while 91% of fully AI-generated submissions were flagged, the average detection within each paper was only 54.8% and only half of those flagged papers would have been referred for academic misconduct. Similar studies since then have continued to show the same types of results. And if detection isn’t possible, setting an absurd line as referred to by Corbin et al is ever more incongruous. There is no reliable way to indicate whether a student has stopped at the point of using AI for brainstorming or has engaged critically with AI paraphrased output. Some may read this and think that it’s game over, however if we embrace these challenges and adapt our approaches, we find solutions that are fit for purpose.

From Fear to Framework: The Two-Lane Approach

So, what is the solution? Our research explored whether the two-lane approach developed by Liu and Bridgeman would work at Abertay, where:

• Lane 1: Secure Assessments would be conducted under controlled conditions to assure learning of threshold concepts and
• Lane 2: Open Assessments would allow unrestricted use of GenAI.

Our case studies revealed three distinct modes of GenAI integration:

• AI Output Only – Students critiqued AI-generated content without using GenAI themselves. This aligned with Lane 1 and a secure assessment method focusing on threshold concepts.
• Limited Collaboration – Students used GenAI for planning and a minimal piece of output within a larger piece of assessment, which did not allow GenAI use. Students developed some critical thinking, but weren’t able to apply this learning to the whole assessment.
• Unlimited Collaboration – Students were fully engaged with GenAI, with reflection and justification built into the assessment. Assessment designers reporting that students produced higher quality work and demonstrated enhanced critical thinking.

Each mode reflected a different balance of trust, control, and pedagogical intent. Interestingly, the AI Output pieces were secure and used to build AI literacy while meeting PSRB requirements, which asked for certain competencies and skills to be tested. The limited collaboration had an element of open assessment, but the percentage of the grade awarded to the output was minimal, and an absurd line was created by asking for no AI use in the larger part of the assessment. Finally, the assessments with unlimited collaboration were designed because those colleagues believed that writing without GenAI was not authentic, and they believed that employers would expect AI literacy skills, perhaps not misplaced based on the figure given in O’Mahony’s blog.

Reframing the Narrative: GenAI as Opportunity

We see the need to treat GenAI as a partner in education, one that encourages critical reflection. This will require carefully scaffolded teaching activities to develop the AI literacy of students and avoid cognitive offloading. Thankfully, ways forward have begun to appear, as noted in the work of Gerlick and Jose et al.

Conclusion: From Wicked to ‘Witch’ lane?

As educators, we have a choice. We can resist, decouple from GenAI or we can choose to lead the narrative strategically and optimistically. Although the pathway forward may not be a yellow brick road, we believe it’s worth considering which lane may suit us best. The key is that we don’t do this in isolation, but we take a pragmatic approach across our entire degree programme considering the level of study and the appropriate AI literacy skills.

GenAI acknowledgement:
Microsoft Copilot (https://copilot.microsoft.com) – used to create a draft blog from our research paper.

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