Exploring Artificial Intelligence as a Remedy to the Heavy Teaching Workloads Caused by Massification of Ugandan Public Universities - Namutebi, Edith (East African Journal of Education Studies, 2024)
Universities worldwide, particularly public universities in Uganda are facing a dilemma in which their massification has far outstripped the growth of their academic service delivery capacity, especially their actual teaching staff size. Consequently, most lecturers are struggling with heavy teaching workloads resulting from large class sizes of 100 to 300 or more students created by massification per course unit, especially at the undergraduate level. These workloads have overstretched most lecturers’ ability to teach effectively and limited their career growth by keeping them too busy to conduct research and participate in community service. The dilemma is faced at the time when Industry 4.0 has developed Artificial Intelligence (AI), which can execute different tasks, including teaching tasks in much the same way as human beings perform them. Drawing on the AI job replacement theory complemented by UTAUT and TOE, this study employed a cross-sectional questionnaire survey involving 325 respondents (deans, heads of department [HODs] and lecturers) randomly selected from five randomly selected public universities to analyse awareness of the teaching tasks AI can execute to reduce faculty members’ workload without replacing them, acceptance of AI to perform these tasks, and hindrances to its adoption. Findings from the descriptive analysis indicate that at least 74% of the deans, HODs, and lecturers were highly aware of the teaching tasks AI can perform. Most of these respondents accept AI to perform such teaching tasks that do not involve a human touch as an online search for research and lecture content, lecture dictation, student assessment and evaluation, and grading of marks. They, however, did not accept AI to execute teaching tasks that involve the human touch such as lecture planning, facilitating tutorials and discussions, assessing students’ interpersonal weaknesses that affect learning, and feedback provision. These findings allude to a need to adopt AI to execute only the teaching tasks it is accepted to perform and leave to the lecturers all the tasks they do not accept to perform. Adopting AI this way is bound to relieve the teaching workload allocated to lecturers as massification intensifies. The findings indicate, however, that AI adoption is hindered by different factors, including lack of strategic, ethical, and policy guidelines, and lack of funds and skills required to operate it. These findings point to a need for the management of Uganda’s public universities to adopt AI by lobbying the government for more funding, mobilizing necessary funds internally, training faculty members in using AI, and encouraging all of them to accept it by explaining the role it is capable of playing in reducing workloads and erasing their fear that AI could replace them