Artificial intelligence (AI) proposals to counter AI cheating by students in non-invigilated assessment

Since ChatGPT3′s chatbot interface appeared in November 2022, AI cheating has re-incarnated a challenge for academia. It is accentuated in non-invigilated contexts during term as projects, essays and other documents are submitted (possibly) prior to proctored examinations. Cheating can be interpreted strategically as the institutional level, whereas its tactical expression examined in this article applies at the student-instructor interface. Here, we turn AI upon itself to see if it can address the dilemma it has created. A literature review regarding AI-facilitated cheating precedes a record of public and initial academic reactions. Having calibrated the problem, a method for addressing it is advanced. It enlists a set of AI large language models (LLMs) and search engines to rank six techniques by effectiveness in countering AI cheating. Analysis includes: descriptive assessment of model outputs; frequency counting, ranking and determination of unique and common techniques; and qualitative comparison of the suggestions tendered. The discussion relates the characteristics of the composite approaches to traditional assessment and evaluation. Ideas for ongoing research, and key recommendations form the conclusions.