*Zeelaf Shahid, Soobia Saeed and Rubina Ghani

Abstract:
Background: Given recent developments such as the recurrent academic failure among medical students, higher educational institutions are addressing ways to keep these students continuously motivated, as well as boost their emotional resilience and cognitive engagement. This study proposes the Counselling Cell Model through which habitual failures are detected, predicted, and remediated by data-based interventions. Artificial intelligence (AI) is utilized in this model to combine demographic, academic, and psychometric data to categorise students’ motivation, stress levels, and performance patterns. Using predictive analytics with CGPA, engagement and psychological parameters, students are divided into low-, moderate-, and high-risk groups from the very beginning.
Methods: In a cross-sectional study conducted on medical students, the researchers utilised structured questionnaires to collect data and conducted AI-assisted data analysis.
Results: The results show a huge drop in motivation amongst medical students from first to final year, leading to chronic burnout with many opting to quitting their studies. The AIenabled analysis can detect early at-risk students and customise strategies such as counselling to enhance and support their retention, mental well-being, and academic performances.
Conclusion: The adoption of AI in the field of counselling offers a revolutionary potential for medical education and academics. With the help of AI-guided counselling, appropriate interventions can be arranged for students who struggle academically, mould their unrealised potential, and build emotionally healthy future physicians.

Keywords: Medical students, academic failure, predictive analytics, integrating AI