Artificial learning: how sessional academics view contract cheating and digital academic misconduct in assessment

This study investigated sessional academics’ perceptions of student misconduct in tertiary education assessments, focusing on contract cheating, misuse of generative artificial intelligence (GenAI) and unauthorised use of smart devices. Using a qualitative research design grounded in the interpretivist paradigm, interviews were conducted with 20 experienced sessional academics from diverse institutions in Sydney. Participants identified misconduct as driven by a combination of contextual pressures, institutional shortcomings and student-specific challenges, such as language barriers and fear of failure. They reported relying on intuitive cues – disconnected content, generic writing, and fake references – to detect misconduct while expressing concerns over limited institutional support and unreliable detection tools. Recommendations include rethinking assessment design, enhancing student support and redefining the meaning and objective of ‘education and learning’. A significant theme was the call for systemic educational reform, including redefining learning outcomes to emphasise critical thinking, ethical reasoning and real-world skills. The findings underscore the need for proactive, pedagogically grounded responses to academic integrity challenges in an era increasingly shaped by digital technologies.