STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews: A Journal of Pharmaceutical Science Review Article

Harnessing AI and NLP to Transform Pharma Education Personalization, Learning, and Skill Development

  1. Somil Shukla Department of Pharmacy, SCPM College of Pharmacy, Gonda
  2. Sujeet Pratap Singh Department of Pharmacy, SCPM College of Pharmacy, Gonda
  3. Pramod Mishra Department of Pharmacy, SCPM College of Pharmacy, Gonda
  4. Tarkeshwar Prasad Shukla Department of Pharmacy, SCPM College of Pharmacy, Gonda

Abstract

Particularly with NLP technologies, it is revolutionizing pharmaceutical education, enhancing human creativity, personalizing learning, and improving student outcomes. AI models like those from Open AI’s Chat GPT are increasingly integrated into educational practices that offer a solution to issues, such as teacher shortages, resource limitations, and the inefficient use of traditional teaching methods. This paper explores the diverse ways through which AI and NLP technologies are transforming pharma education within the fields of research, patient counseling, and skill development. Such prime applications of virtual labs and simulations offer affordable and scalable alternatives to the physical laboratory while many students could not access physical laboratories due to the COVID-19 pandemic. Such AI-driven interactive learning platforms for pharmacy students, such as those using ChatGPT-4, are more empowered to simulate real-life patient counseling scenarios. Predictive learning analytics, dependent on historical student information, enables student performance to be monitored and improved and can predict at-risk students through interventions. AI in facilitating content development and summarization helps make any academic resources and other learning materials available faster. From GPT-1 to GPT-4 and beyond, the evolution of the AI models discusses how they have become more sophisticated as they are currently capable of performing more complex tasks. This includes producing human-like responses to analyzing emotional sentiments. All these contributions are leaning toward a more dynamic, personalized educational experience. This paper concludes that AI and NLP are necessary tools to modernize pharmaceutical education, offering innovative solutions to long-standing challenges while paving the way for more effective, engaging, and accessible learning environments.

Keywords

References (31)

  1. Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, bin Saleh K, et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. 2023;19(8):1236-1242. doi:10.1016/j.sapharm.2023.05.016
  2. Lesage, Jonathan, Robert Brennan, Sarah Elaine Eaton, Beatriz Moya, Brenda McDermott, Jason Wiens, and Kai Herrero. "Exploring natural language processing in mechanical engineering education: Implications for academic integrity." International Journal of Mechanical Engineering Education 52, no. 1 (2024): 88-105 doi:1177/03064190231166665
  3. Fuchs K. Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse? Frontiers in Education. 2023;8. doi:10.3389/feduc.2023.1166682
  4. Mortlock R, Lucas C. Generative artificial intelligence (Gen-AI) in pharmacy education: Utilization and implications for academic integrity: A scoping review. Exploratory Research in Clinical and Social Pharmacy. 2024;15:100481. doi:10.1016/j.rcsop.2024.100481
  5. Zhang X, Tsang CCS, Ford DD, Wang J. Student Pharmacists’ Perceptions of Artificial Intelligence and Machine Learning in Pharmacy Practice and Pharmacy Education. American Journal of Pharmaceutical Education. 2024;88(12):101309. doi:10.1016/j.ajpe.2024.101309
  6. Cain J, Malcom DR, Aungst TD. The Role of Artificial Intelligence in the Future of Pharmacy Education. American Journal of Pharmaceutical Education. 2023;87(10):100135. doi:10.1016/j.ajpe.2023.100135
  7. Sheikh, Rana Afreen, Sakshi Gunavant Pawar, Aishwarya Sunil Lakras, and Gauri Rajesh Kasture. "Chat GPT curse or blessings." International Research Journal of Innovations in Engineering and Technology vol 7, no. 9 (2023): 150-152 .
  8. Liu Y, Maier W, Minker W, Ultes S. Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge. Lecture Notes in Electrical Engineering. 2022:67-81. doi:10.1007/978-981-19-5538-9_5
  9. Fuchs, Kevin. "Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse?." In Frontiers in Education, vol. 8, p. 1166682. Frontiers Media SA, 2023.
  10. Johnson, Douglas, Rachel Goodman, J. Patrinely, Cosby Stone, Eli Zimmerman, Rebecca Donald, Sam Chang et al. "Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model." Research square (2023).
  11. Moritz, Elan. "Chatting with chat (GPT-4): Quid Est understanding." London, Canada: Centre for Digital Philosophy at Western University (2024).
  12. Pant, Dipendra, and Suresh Kumar Mukhiya. "Technical Evaluation of GPT-4 and GPT-4-Turbo Reflective Listening Response Generation with the Counsel Chat Dataset." (2024).
  13. Eukel, Heidi N., Jeanne E. Frenzel, Elizabeth T. Skoy, Rebecca L. Focken, and Alicia L. Fitz. "An introductory pharmacy practice experience using simulated patient care activities in a pharmaceutical care laboratory environment." Currents in Pharmacy Teaching and Learning 6, no. 5 (2014): 682-691.
  14. Reid, N.; Shah, I. The role of laboratory work in university chemistry. Educ. Res. Pract.2007, 8, 172–185.
  15. Bretz, S.A. Evidence for the Importance of Laboratory Courses. Chem. Educ.2019, 96, 193–195.
  16. Ma, J.; Nickerson, J.V. Hands-On, Simulated and Remote Laboratories: A Comparative Literature Review. ACM Comput. Surv.2006, 38, 1–24
  17. Schneider, Jennifer, Chelsea Felkai, and Irene Munro. "A comparison of real and virtual laboratories for pharmacy teaching." Pharmacy 10, no. 5 (2022): 133.
  18. https://medium.com/@wijesingheds/pharm-counsel-ai-a-new-era-of-interactive-learning-in-pharmacy-education-df77c1915166
  19. Oestreich JH, Guy JW. Game-Based Learning in Pharmacy Education. Pharmacy. 2022;10(1):11. doi:10.3390/pharmacy10010011
  20. https://elearningindustry.com/why-is-game-based-learning-important#:~:text=Game%2Dbased%20learning%20is%20an,thinking%20and%20problem%2Dsolving%20skills.
  21. https://www.sas.com/en_in/insights/analytics/predictive-analytics.html#:~:text=Predictive%20analytics%20is%20the%20use,will%20happen%20in%20the%20future
  22. Sghir, Nabila, Amina Adadi, and Mohammed Lahmer. "Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)." Education and information technologies 28, no. 7 (2023): 8299-8333.
  23. : A comprehensive survey." Expert systems with applications 165 (2021): 113679. El-Kassas, Wafaa S., Cherif R. Salama, Ahmed A. Rafea, and Hoda K. Mohamed. "Automatic text summarization
  24. Cao, Yihan, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, and Lichao Sun. "A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt." arXiv preprint arXiv:2303.04226 (2023).
  25. Patel, Chetna R., Sajal K. Pandya, and Brijesh M. Sojitra. "Perspectives of ChatGPT in pharmacology education, and research in health care: a narrative review." Journal of Pharmacology and Pharmacotherapeutics 14, no. 3 (2023): 171-177
  26. https://springsapps.com/knowledge/artificial-intelligence-in-pharmacy-use-cases-examples-challenges
  27. https://www.neurology.org/doi/full/10.1212/WNL.0000000000012570
  28. Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: CHIL ‘20: Proceedings of the ACM Conference on Health, Inference, and Learning, Toronto, ON, Canada, April 2–4, 2020.New York, NY: Association for Computing Machinery, 2020;151–159
  29. Evans H, Snead D. Understanding the errors made by artificial intelligence algorithms in histopathology in terms of patient impact. npj Digital Medicine. 2024;7(1). doi:10.1038/s41746-024-01093-w
  30. Hall KK, Fitall E. Artificial Intelligence and Diagnostic Errors. PSNet [internet]. Rockville (MD): Agency for Healthcare Research and Quality, US Department of Health and Human Services. 2020
  31. https://www.hks.harvard.edu/more/student-life/student-stories/using-ai-combat-medical-misdiagnosis-and-improve-patient-care
Support