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Aim/Purpose: The purpose of the present study is to prioritize the inhibiting and motivating factors underlying the adoption of AI based teaching and learning solutions by teachers in the higher education sector of India. Background: AI based teaching and learning solutions are amongst the most important educational innovations. The intervention of AI in instructional methods can result in personalized teaching and learning experiences. AI enabled teaching and learning systems can give teachers a better understanding regarding their students’ learning abilities, learning styles and progress. Methodology: The Analytic Hierarchy Process (AHP) is employed to find the relative importance of inhibiting and motivating factors. The primary data for making the pair-wise comparisons between the factors were obtained from a convenient sample of 32 teachers, teaching in various higher educational institutions (HEIs) in the National Capital Region (NCR) of Delhi, India. Contribution: Though, the acceptance of AI based solutions has been studied in other contexts such as retail, banking, ecommerce, and so on; nonetheless, the acceptance of AI in the education sector has not grabbed much attention of researchers. Hence the study has made worthwhile contributions to the literature as it has specifically focused on the adoption of AI based teaching methods by teachers in higher education Findings: The findings suggest that institutional barriers are the major inhibitors and recognition is the main motivator that affect teachers’ behaviour towards adopting AI based teaching solutions. Overall, the findings of the study highlight the importance of institutional support in terms of resources, time, and recognition that may be provided to the teachers so that they can willingly integrate AI based methodologies into their teaching. Recommendations for Practitioners: The study provides several implications for HEIs and developers of AI based educational solutions. The HEIs should provide adequate support to their teachers in terms of financial support, infrastructure and technical support. The developers should focus on developing such solutions that are compatible with the teachers’ existing work style. Recommendation for Researchers: Future studies can employ statistical techniques such as multiple regression analysis or structural equation modelling to examine the impact of these factors on the actual use behaviour of teachers regarding AI based teaching methods. More diversified samples that are statistically significant in size, can be considered to examine the teachers’ behaviour regarding AI based instructional methods. Impact on Society: AI technology can play a pivotal role in reshaping and remodeling higher education. AI is the technology of todays’ times that has the capability of transforming the instructional methods. The educators need to understand that nowadays, teaching and learning are heading towards creative styles that embrace the use of innovative technologies such as AI. Future Research: The adoption of AI in the field of education is at a very nascent stage in India, constant changes are likely to happen in the factors influencing the adoption of AI enabled teaching solutions. Future studies may come up with a more holistic model of factors to address this research problem.
Gupta et al. (Wed,) studied this question.