Emotional Intelligence in the Digital Age: Harnessing AI for Students’ Inner Development


  • Nayiri Keshishi University of Surrey
  • Dr Sarah Hack University of Surrey




Artificial Intelligence, Development, Digital, Emotional intelligence, Skills


Artificial Intelligence (AI) presents both opportunities and challenges in fostering emotional intelligence (EI) in students. EI, vital for personal, academic, and professional success, involves recognising and regulating emotions in oneself and others. This opinion piece explores AI's potential and challenges in enhancing EI. AI's role in higher education and its various applications, including assessment and prediction, are discussed. The article also addresses the recent proliferation of Generative AI (GenAI) tools, which generate diverse content types and have sparked debates in education. AI's potential in developing each component of Goleman's EI model (1998) is examined, focusing on empathy, social skills, self-awareness, self-regulation, and motivation. AI-driven applications, such as those aiding individuals in recognising and managing emotions, practising empathy, and tailoring educational content to foster motivation, are highlighted. The piece also acknowledges concerns, such as ethical and privacy considerations in data collection, potential biases in AI algorithms, and the risk of overreliance on AI. In conclusion, we advocate for a balanced approach that combines AI tools with traditional teaching methods and human interactions to cultivate EI effectively whilst managing associated risks.


Ahmed, A., Ali, N., Aziz, S., Abd-Alrazaq, A. A., Hassan, A., Khalifa, M., Elhusein, B., Ahmed, M., Ahmed, M.A.S., & Househ, M. (2021). A review of mobile chatbot apps for anxiety and depression and their self-care features. Computer Methods and Programs in Biomedicine Update, 1, 100012.

Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communication, 10(311) (2023). https://doi.org/10.1057/s41599-023-01787-8

Awatramani, J., & Hasteer, N. (2020). Facial expression recognition using deep learning for children with Autism Spectrum Disorder. In IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India. (pp. 35-39). doi: 10.1109/ICCCA49541.2020.9250768.

Compton, M. (2023, July 28). Generative AI practicals: Using ChatGPT as a dialogic tutor. Heducationist. https://mcompton.uk/

Crompton, H., & Burke, D. Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education 20, 22 (2023). https://doi.org/10.1186/s41239-023-00392-8

Fido, D., & Wallace, L. (2023). The Unique Role of ChatGPT in Closing the Awarding Gap. The Interdisciplinary Journal of Student Success.

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 1-15.

Furze, L. (2023, January 26). Teaching AI Ethics. Leon Furze. https://leonfurze.com/

Goleman, D. (1998). Working with emotional intelligence. Bantam.

Goleman, D. (2001). Emotional intelligence: Issues in paradigm building. The emotionally intelligent workplace, 13, 26.

Hosseini, D. (2023, August 08). Generative AI: a problematic illustration of the intersections of racialized gender, race, ethnicity. Digital Education Practices. https://digitaleducationpractices.com/

MacCann, C., Jiang, Y., Brown, L. E., Double, K. S., Bucich, M., & Minbashian, A. (2020). Emotional intelligence predicts academic performance: A meta-analysis. Psychological bulletin, 146(2), 150.

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Available at http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

Middya, A. I., Nag, B., & Roy, S. (2022). Deep learning based multimodal emotion recognition using model-level fusion of audio–visual modalities. Knowledge-Based Systems, 244, 108580.

Schutte, N.S., & Stilinović, E.J. (2017). Facilitating empathy through virtual reality. Motivation and Emotion (41), 708–712. https://doi.org/10.1007/s11031-017-9641-7

Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungs problem. Proceedings of the London Mathematical Society, 2(1), 230–265.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 443–460.

World Economic Forum (2023, May 1). Future of jobs 2023: These are the most in-demand skills now - and beyond. https://www.weforum.org/agenda/2023/05/future-of-jobs-2023-skills/

Zheng, Z., Liao, L., Deng, Y., & Nie, L. (2023). Building Emotional Support Chatbots in the Era of LLMs. https://doi.org/10.48550/arXiv.2308.11584