Enhancing Academic Advising Through AI: A Conceptual Model for Furhat Robot Adoption in Higher Education

Artificial Intelligence (AI) in post-secondary education is revolutionizing academic advising through accessible, efficient, and tailored help for students. Traditional advising methodologies are inhibited by advisor availability (AA) issues, administrative inefficiencies (AE), language obstacles (LB), and problems related to AI dependability and trust (RT). Through a controlled survey, this study investigates determinants of AI-based academic advising (AP) preference among university students. It develops a new conceptual model that explains AI adoption. The results of hypothesis testing concur with primary relationships: Reduced Advisor Availability (AA) has a significant impact on Administrative Efficiency (AE) (r = 0.772, p < 0.05) and AI augments process flow while reducing human advisor reliance. Moreover, Higher Administrative Efficiency (AE) is directly related to AI Preference (AP) (χ² = 18.32, p = 0.028), implying that students prefer AI if it makes the advising process easier. However, Language Barriers (LB) failed to have any significant impact on AI Preference (p > 0.05), which implies that language access alone cannot help increasing AI adoption. Increased AI Reliability and trust (RT) has a positive influence on AI Preference (AP), and it is critical to ensure reliable, unbiased AI recommendations in academic advising. The new conceptual model integrates these results, proving that AA, AE, LB, and RT make decisions regarding students' AI adoption. Universities can achieve optimal adoption by improving AI reliability, administrative integration, and hybrid AI-human advising designs. Future research must examine long-term adoption trends in AI, cross-cultural variations, and ethical concerns to advance AI-based advising systems for higher education.