Digital-k: AI and the implications for assessment
Queensland Teachers' Journal, Vol 128 No 8, 2 November 2023, page no. 25
In many respects, the implications of AI for assessment practices have been more thoroughly considered than other, broader issues. Both universities and schools have been aware of opportunities for students to use AI and other digital tools to “cheat” for some time
In fact, the detection of cheating is taken so seriously in universities that a number of institutions have resourced their faculties with personnel who have a key role in working with students about the use of AI in assessment pieces.
Schools do not have the capacity to do this without additional funding. If AI use is to be policed in schools, this level of resourcing/personnel must be delivered.
Perhaps more to the point, however, schools deal with younger students and therefore, appropriately, often take a more pastoral approach when cheating is detected, with the goal of educating the student on why cheating is unethical, rather than simply punishing them for having cheated.
Lodge et al (https://www.linkedin.com/pulse/assessment-redesign-generative-ai-taxonomy-options-viability-lodge) note that there are six options for educators trying to find ways of dealing with AI and its impact on assessment:
- Ignore
- Ban
- Invigilate
- Embrace
- Design around
- Rethink.
Given that ignoring and banning are unlikely to produce solutions, and invigilating requires investment of considerable resources that might be better deployed in facilitating quality teaching and learning, it would appear that educators have no option but to embrace, design around, and rethink.
One rethink option open to teachers in schools is to shift to more process-based assessment models such as oral examinations, which place less emphasis on the artefact and more on the learning underlying it. Such changes are, however, likely to be highly resource intensive and could potentially overwhelm teachers with even more assessment and moderation work (i.e. individual assessment interviews with a class of 25 senior students).
AI could possibly help ameliorate assessment-related workload. Some authors have, for example, made suggestions that integration of AI into learning environments can develop complex, multidimensional models that instantaneously summarise the learning status of individuals across subject areas in a way that might conceivably facilitate more precise instructional diagnosis.
The solution for designing and implementing effective 21st century assessment paradigms is, at least in the school context, likely to lie somewhere between process-focused and AI-mediated assessment.