Assessment practices resistant to inappropriate AI use in Open Distance eLearning: insights from Zimbabwe Open University

The rapid integration of artificial intelligence into educational practices has raised significant concerns among educators regarding the integrity and effectiveness of traditional assessment methods in the Zimbabwean education sector. This has raised concerns regarding the reliability of submitted assignments, research articles, research projects and other formative assessments, especially as some students use AI to write assignments and other academic tasks. Accordingly, this paper sought to identify assessment practices perceived as effective in promoting authentic student learning and minimizing the inappropriate use of artificial intelligence in Open Distance eLearning environments. A qualitative and instrumental case study design was employed with in-depth interviews and focus group discussions. Seven lecturers, fourteen students and a quality assurance coordinator were purposively sampled from the Masvingo Regional Campus of the Zimbabwe Open University. This study used Constructivist Learning Theory (Piaget, Vygotsky), as lens to understand effective assessment strategies that minimize inappropriate use of artificial intelligence. The findings indicate that in class tests/ assignments, presentations, creation of tasks that can be assessed by observation of student performance, interviews and engaging students in debates, panel discussions and seminars that require students to prepare their arguments and support them can be resistant to inappropriate use of Artificial Intelligence in Open Distance learning. The study suggests that efforts should be made to incorporate artificial intelligence into teacher training curricula in colleges and universities to maintain academic integrity.