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- We developed a mathematical model based on the classical SEIR (Susceptible–Exposed–Infective–Recovered) framework to predict teachers’ readiness to adopt Artificial Intelligence (AI) in educational settings, with a specific focus on Nigeria. In this context, we reinterpret the compartments as follows: Unaware population (S) represents teachers who are not yet aware of AI’s potential in education; Aware (E) includes teachers who are informed but undecided about AI adoption; Adopters (I) are those who have begun integrating AI into their teaching practices; and Discontinued Users (R) are teachers who previously used AI but have ceased due to resource constraints or lack of institutional support. We meticulously analyzed the model’s properties, including positivity, boundedness, and stability, to ensure the accuracy and applicability of the results. Additionally, a comprehensive sensitivity analysis was performed to identify key parameters influencing the dynamics of AI adoption. Numerical simulations were utilized to demonstrate the effects of these parameters on the teacher population over time. Our results reveal that a higher teacher attrition rate decreases the unaware population initially but leads to a resurgence after a critical threshold is crossed. Furthermore, the rapid transition from awareness to adoption was instrumental in accelerating AI integration, whereas sustained resource availability emerged as a crucial determinant for maintaining long-term adoption. This study provides valuable insights into the nuanced dynamics of AI adoption among educators, highlighting the necessity for targeted interventions and effective resource allocation to facilitate successful AI integration in teaching. The findings have significant implications for policymakers and educational institutions aiming to promote the adoption of AI-enhanced pedagogical practices, underscoring the importance of strategic planning and support mechanisms to foster a conducive environment for technology-driven educational advancements.
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- The use of generative artificial intelligence (GenAI) chatbots in brands is growing exponentially, and higher education institutions are not unaware of how such tools effectively shape the attitudes and behavioral intentions of students. These chatbots are able to synthesize an enormous amount of data input and can create contextually aware, human-like conversational content that is not limited to simple scripted responses. This study examines the factors that determine chatbot brand trust in the adoption of GenAI in higher education. By extending the Technology Acceptance Model (TAM) with the construct of brand trust, the study introduces a novel contribution to the literature, offering fresh insights into how trust in GenAI chatbots is developed within the academic context. Using the convenience sampling technique, a sample of 609 students from public universities in North Central and Southwestern Nigeria was selected. The collected data were analyzed via partial least squares structural equation modelling. The results indicated that attitudes toward chatbots determine behavioral intentions and GenAI chatbot brand trust. Surprisingly, behavioral intentions do not affect GenAI chatbot brand trust. Similarly, the perceived ease of use of chatbots does not determine behavioral intention or attitudes toward GenAI chatbot adoption but rather determines perceived usefulness. Additionally, the perceived usefulness of chatbots affects behavioral intention and attitudes toward GenAI chatbot adoption. Moreover, social influence affects behavioral intention, perceived ease of use, perceived usefulness and attitudes toward GenAI chatbot adoption. The implications of the findings for higher education institutions are that homegrown GenAI chatbots that align with the principles of the institution should be developed, creating an environment that promotes a positive attitude toward these technologies. Specifically, the study recommends that policymakers and university administrators establish clear institutional guidelines for the design, deployment, and ethical use of homegrown GenAI chatbots, ensuring alignment with educational goals and safeguarding student trust.
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