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Open Access Review

Enhancing University Students’ Mental Health under Artificial Intelligence: Principles of Behaviour Therapy

Mubashir Zafar *

  1. Department of Family and Community Medicine, College of Medicine, University of Hail, Saudi Arabia

Correspondence: Mubashir Zafar

Academic Editor: Félix Fernando Monteiro Neto

Received: March 13, 2024 | Accepted: May 19, 2024 | Published: May 24, 2024

OBM Neurobiology 2024, Volume 8, Issue 2, doi:10.21926/obm.neurobiol.2402225

Recommended citation: Zafar M. Enhancing University Students’ Mental Health under Artificial Intelligence: Principles of Behaviour Therapy. OBM Neurobiology 2024; 8(2): 225; doi:10.21926/obm.neurobiol.2402225.

© 2024 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.


The increasing prevalence of mental health issues among university students has become a growing concern globally. This review explores the potential of Artificial Intelligence (AI) integrated with principles of behaviour therapy to address mental health challenges among university students. The paper examines how AI technologies, including chatbots, virtual reality, and machine learning algorithms, can be harnessed to provide accessible, personalized, and effective mental health interventions. Furthermore, it discusses applying behaviour therapy principles within AI-driven mental health interventions, focusing on techniques such as cognitive restructuring, exposure therapy, and reinforcement strategies. The review highlights the promising outcomes and challenges of integrating AI and behaviour therapy principles in university mental health services, emphasizing the need for ethical considerations, privacy protection, and cultural sensitivity. By synthesizing current research findings and theoretical frameworks, this paper provides insights into the potential of AI-driven behaviour therapy interventions to enhance university students’ mental health and well-being.


University students; mental health; artificial intelligence; principles; behaviour therapy

1. Introduction

The prevalence of mental health issues among university students has reached alarming levels in recent years, constituting a significant public health concern worldwide. Research indicates a substantial increase in the rates of depression, anxiety, and other psychological disorders among university populations [1]. Various factors contribute to this phenomenon, including academic pressures, social isolation, financial stressors, and the transition to adulthood. The demanding nature of university life, coupled with limited access to mental health resources, exacerbates the vulnerability of students to mental health challenges.

In response to this growing crisis, there is an urgent need for innovative and accessible mental health interventions tailored to the unique needs and preferences of university students. Traditional approaches to mental health care, characterized by face-to-face therapy sessions and resource-intensive interventions, often encounter barriers such as stigma, limited resources, and geographical constraints. Consequently, there is a pressing demand for alternative modalities to overcome these barriers and effectively support university students.

Artificial Intelligence (AI) has emerged as a promising paradigm for transforming mental health care delivery, offering scalable, personalized, and cost-effective solutions. AI-driven technologies, including chatbots, virtual reality simulations, and machine learning algorithms, can potentially revolutionize the landscape of mental health interventions for university students. These technologies can provide on-demand support, deliver evidence-based interventions, and adapt dynamically to users’ needs and preferences.

Moreover, integrating principles of behaviour therapy within AI-driven interventions holds promise for addressing university students’ mental health challenges. Behaviour therapy, grounded in empirical research and cognitive-behavioural principles, emphasizes modifying maladaptive thoughts, behaviours, and emotional responses to promote psychological well-being [2]. By integrating behaviour therapy techniques such as cognitive restructuring, exposure therapy, and positive reinforcement into AI-driven platforms, mental health interventions can be tailored to address university students' specific needs and concerns.

Studies have shown that AI-based interventions can be as effective as traditional therapy in managing stress and anxiety among students, with the added benefit of convenience and reduced stigma [3,4,5,6]. Fitzpatrick et al. [4] found that AI-powered mental health apps improved self-reported mental health outcomes and coping strategies among university students. Torous et al. [5] emphasize the potential of AI to deliver personalized and adaptive behavioral therapies, offering targeted support for a diverse student population. Nadarzynski et al. [6] highlight the potential of AI-based interventions to address students' unmet mental health needs by providing scalable, efficient, and data-driven support. By leveraging AI, universities can address the mental health needs of their student populations more effectively, providing scalable and efficient support systems that complement existing therapy services.

This review aims to explore the intersection of AI and behaviour therapy principles in enhancing university students’ mental health. This paper seeks to elucidate the potential benefits and challenges associated with integrating AI and behaviour therapy in university mental health services by synthesizing current research findings, theoretical frameworks, and practical applications. Through a comprehensive examination of the literature, this review aims to provide insights into the efficacy, feasibility, and ethical considerations of AI-driven behavior therapy interventions for university students (Table 1).

Table 1 Description of Selected Studies include in the review article.

2. Method

To conduct this review, a systematic approach was employed to identify relevant literature published between 2010 and 2024. Electronic databases, including PubMed, PsycINFO, and Google Scholar, were systematically searched using a combination of keywords and Boolean operators. The following search terms were utilised: “university students,” “mental health,” “artificial intelligence,” “behaviour therapy,” and related variations.

Studies were included based on their relevance to the intersection of AI and behaviour therapy in university mental health settings. Inclusion criteria encompassed empirical research articles, review papers, and theoretical discussions that explored the application of AI technologies and behaviour therapy principles in addressing mental health challenges among university students.

Following the initial search, duplicates were removed, and titles and abstracts were screened to assess their relevance to the research topic. Full-text articles meeting the inclusion criteria were then retrieved and reviewed in detail. Additional articles were identified through manual searches of reference lists and citation tracking of relevant papers.

Data extraction involved synthesizing key findings, methodologies, and implications from the selected articles. The review process adhered to established guidelines for systematic literature reviews, ensuring rigor and transparency in the selection and synthesis of evidence.

The findings of this review provide insights into the potential of AI-driven behaviour therapy interventions to enhance university students’ mental health and well-being while highlighting the need for further research and collaboration in this burgeoning field.

3. Discussion

3.1 Artificial Intelligence in Mental Health Interventions

AI technologies have revolutionized mental health interventions, offering scalable and accessible solutions for university students [4]. Chatbots, virtual reality, and machine learning algorithms are among the innovative AI tools utilized to deliver personalized mental health support.

3.2 Principles of Behaviour Therapy

Behaviour therapy principles, rooted in cognitive restructuring and exposure therapy, have demonstrated efficacy in treating various mental health disorders among university students [2]. These principles emphasize modifying maladaptive behaviours and cognitions to promote psychological well-being.

3.3 Integration of AI and Behaviour Therapy Principles

The integration of AI and behaviour therapy principles offers a promising approach to enhancing mental health interventions for university students [7]. AI-driven platforms can deliver tailored interventions based on individual needs, incorporating behaviour therapy techniques such as cognitive restructuring and positive reinforcement.

3.4 Promising Outcomes and Challenges

Studies have reported favorable outcomes associated with AI-driven behaviour therapy interventions for university students’ mental health [8]. However, challenges including privacy concerns, ethical considerations, and cultural sensitivity, require careful attention to ensure the effectiveness and accessibility of these interventions.

One study examined the use of AI in supporting Cognitive Behavioral Therapy (CBT) for university students, highlighting AI's ability to tailor interventions to individual needs and provide personalized exercises [9] (Frontiers). This approach can improve therapy outcomes and enhance students' engagement in the therapeutic process. AI can also offer continuous monitoring and feedback, aiding therapists in making informed decisions about their patients' progress and needs.

AI-powered chatbots and mental health apps are another area of interest in recent literature. These tools provide students immediate access to mental health support, especially outside traditional therapy hours. A study on AI-based chatbots found that these tools can effectively manage stress and anxiety among university students, offering evidence-based interventions and self-help resources [10] (BioMed Central). Chatbots can also serve as a first point of contact for students hesitant to seek therapy, helping to reduce stigma and barriers to access.

Moreover, AI-driven predictive analytics have been employed to identify students at risk of mental health issues and offer proactive support. By analyzing data from academic performance, social interactions, and self-reported emotions, AI can flag students who may benefit from early interventions and targeted support [11] (PLOS). This proactive approach can help prevent the escalation of mental health issues among university students.

In conclusion, integrating AI and behavior therapy shows great potential in enhancing mental health interventions for university students. By offering personalized support, accessible resources, and proactive monitoring, AI-driven tools can complement traditional therapy and improve students' overall mental health care landscape. Further research and development in this field are essential to maximize the benefits of AI in mental health interventions for university students.

4. Conclusion

The synthesis of AI and behaviour therapy principles holds immense potential to address university students' complex mental health needs. Future research and collaborative efforts are essential to optimize the integration of these approaches while addressing ethical and cultural considerations.

Author Contributions

The author did all the research work of this study.

Competing Interests

The author has declared that no competing interests exist.


  1. Andrews G, Williams AD. Internet psychotherapy and virtual reality interventions for university students: A review of the research and future directions. J Cyber Ther Rehabil. 2015; 8: 171-178.
  2. Beck JS. Cognitive behavior therapy: Basics and beyond. New York, NY: Guilford Publications; 2011.
  3. Hollis C, Morriss R, Martin J, Amani S, Cotton R, Denis M, et al. Technological innovations in mental healthcare: Harnessing the digital revolution. Br J Psychiatry. 2015; 206: 263-265. [CrossRef]
  4. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment Health. 2017; 4: e19. [CrossRef]
  5. Torous J, Wisniewski H, Liu G, Keshavan M. Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: Comparative survey study. JMIR Ment Health. 2018; 5: e11715. [CrossRef]
  6. Nadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Dig Health. 2019; 5. doi: 10.1177/2055207619871808. [CrossRef]
  7. Proudfoot J, Goldberg D, Mann A, Everitt B, Marks I, Gray JA. Computerized, interactive, multimedia cognitive-behavioural program for anxiety and depression in general practice. Psychol Med. 2003; 33: 217-227. [CrossRef]
  8. Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. Utilizing a personal smartphone custom app to assess the patient health questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. JMIR Mental Health. 2015; 2: e8. [CrossRef]
  9. Kooistra A, et al. AI and the future ofcognitive behavioral therapy: A systematic review. J Med Internet Res. 2022; 24: e29995.
  10. Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: A bibliometric analysis. Front Psychol. 2024; 15: 1375294. [CrossRef]
  11. McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a Web-based social anxiety intervention among university students: Randomized controlled trial. J Med Internet Res. 2018; 20: e91. [CrossRef]
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