Location
Kumasi, Ghana & Virtual
Start Date
7-8-2026 11:30 AM
End Date
7-8-2026 12:00 PM
Description
Artificial intelligence (AI) is rapidly transforming higher education, creating opportunities while raising ethical concerns such as bias, privacy violations, academic misconduct and reduced human-centred teaching. This study examined how institutions can balance AI innovation with academic integrity and equity, focusing on lecturers’ ethical dilemmas and institutional readiness. A triangulation design incorporating quantitative and qualitative methods was used. Twelve semi-structured interviews and two focus group discussions involving eighteen participants were conducted across five universities, while 150 questionnaires were distributed, yielding 128 valid responses. Descriptive statistics and thematic analysis were applied. Findings showed that lecturers reported student misuse and plagiarism (78%), AI bias (65%), lack of institutional guidance (72%), reduced teaching quality (47%), and data privacy concerns (52%). Participants also highlighted inadequate training and inconsistent policies, indicating that unregulated AI adoption may undermine fairness and integrity. The study concludes that universities should implement AI policies, strengthen training, ensure transparency, and maintain ethical monitoring to support responsible AI integration in higher education.
Included in
Digital Communications and Networking Commons, Other Computer Engineering Commons, Systems Science Commons
Balancing Innovation and Integrity: Ethical Implications of AI Use in Higher Education
Kumasi, Ghana & Virtual
Artificial intelligence (AI) is rapidly transforming higher education, creating opportunities while raising ethical concerns such as bias, privacy violations, academic misconduct and reduced human-centred teaching. This study examined how institutions can balance AI innovation with academic integrity and equity, focusing on lecturers’ ethical dilemmas and institutional readiness. A triangulation design incorporating quantitative and qualitative methods was used. Twelve semi-structured interviews and two focus group discussions involving eighteen participants were conducted across five universities, while 150 questionnaires were distributed, yielding 128 valid responses. Descriptive statistics and thematic analysis were applied. Findings showed that lecturers reported student misuse and plagiarism (78%), AI bias (65%), lack of institutional guidance (72%), reduced teaching quality (47%), and data privacy concerns (52%). Participants also highlighted inadequate training and inconsistent policies, indicating that unregulated AI adoption may undermine fairness and integrity. The study concludes that universities should implement AI policies, strengthen training, ensure transparency, and maintain ethical monitoring to support responsible AI integration in higher education.
