Visualizing use of generative artificial intelligence in higher education
Neepa K. ShahPurpose
Higher education students are engaged with academic learning and research using generative artificial intelligence (GenAI) applications. A major shift in pedagogical approach surfaces many challenges to academic integrity that urging to redefining assessment model and ethical literacy. This study aims to investigate impact of the GenAI apps among the students using survey of technical and non-technical disciplines. Survey is mainly focused on: use of GenAI apps, variations in observed effectiveness and student’s usage pattern, level of adoption and acceptance of GenAI apps. Reviews of existing literature on the implications of GenAI and emerging need for systemic changes are discussed with reference to result.
Design/methodology/approach
Survey was developed to assess the integration of GenAI apps in the higher education. It was conducted with undergraduate and postgraduate students so as to find the adoption and acceptance rate of GenAI apps among the students; it has been partitioned into two categories: non-technical disciplines and technical disciplines. Students from the social science and pure sciences are in the non-technical disciplines, while students from computer science and engineering students are in the technical discipline. The questionnaire included a mix of multiple-choice questions, scale items and open-ended responses.
Findings
This research is classified for students into technical and non-technical disciplines, revealing notable differences in adoption patterns. First, students from non-technical disciplines are not entirely using GenAI applications. Second, effectiveness of the usage can be analyzed by the purpose of usage of GenAI apps which is shown by graphs. However, technical students show higher inclination for AI usages. Third, regarding adoption or acceptance, many rural students initially adopt it through classmates or roommates. In contrast, students in urban areas, especially in technical disciplines, spend higher hours; here, they accept the technology and update themselves with new technology.
Research limitations/implications
The survey did not assess the accuracy or effectiveness of GenAI outputs. This is a limitation for drawing conclusions about the performance quality of these apps across disciplines.
Originality/value
Students come from different parts of India, so it is also possible that the places where they previously studied lacked facilities such as internet connectivity and stable power supply. This influences the usage. Students from many disciplines participated in the survey, so their perspective to look at the GenAI apps is a unique feature. GenAI apps are accepted among technical students, and non-technical students are gradually adopting. Over-reliance may hinder the individual learning and creativity; it can be an aid to the learning but not as a supplement of human intelligence. Universities face challenges in formulating policies.