Authors :
Vasireddy Surya; Rooma Tyagi; Vineel Sai Kumar Rampally; Deepthi Gonala; Shirish Kumar Gonala
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/3s7yvnpt
Scribd :
https://tinyurl.com/4wrckjdw
DOI :
https://doi.org/10.38124/ijisrt/25apr1781
Google Scholar
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Abstract :
The pharmaceutical industry uses traditional training and evaluation approaches that often lack personalized
learning, interactive training, and immediate performance assessment thus restricting their effectiveness in today’s fast-
paced educational environments. The Solution implements an AI-based chatbot system that supports pharmaceutical
training along with evaluation needs. The chatbot functions as an interactive learning assistant which enables students and
trainees to participate in real-time dialogues while accessing pharma-related assignments along with quizzes that deliver
instant personalized feedback for each user. It leverages a fine-tuned large language model LLaMA 2 which delivers context-
specific accurate results for pharmaceutical inquiries including both unstructured questions and structured learning
content. The system features three operational modes which enable users to interact through domain-specific questions i.e.
Interactive Question & Answers (1), complete multiple-choice quizzes with automatic evaluation and scoring i.e. Quiz Mode
(2) and generate questions for deeper learning and assessment. i.e. Assignment & Training Questionnaire Generator (3). To
evaluate the solution effectiveness, Available open-source datasets used to fine-tune LLaMA model. The chatbot's
performance was assessed through qualitative assessment and its ability to accurately interpret input and generate output.
The AI solution delivers simplified knowledge distribution and assessment during remote training sessions while
simultaneously reducing time requirements for evaluation tasks. Future improvements to this system might include support
for multiple languages as well as real-time analytics integration and learning paths. The chatbot system provides users with
assessment tools and evaluation criteria allowing users to monitor their learning growth at any time from any location. The
system includes interactive training tools such as quizzes together with questionnaires and assignment prompts to get
continuous user participation while reinforcing their knowledge acquisition. Through this intelligent system, the model not
only delivers informative responses but also encourages self-assessment, making it a valuable tool for modern
pharmaceutical training and education.
Keywords :
Pharmaceutical Training, AI-Based Chatbot, Personalized Feedback, Interactive Learning, LLaMA 2 (Large Language Model Meta AI), Real-Time Assessment, Knowledge Distribution, Questionnaire Generator.
References :
- Vasireddy Surya; Rooma Tyagi; Vineel Sai Kumar Rampally; Shirish Kumar Gonala. “Next-Gen Pharma Communication: Revolutionizing Doctor-Pharma Relationships Using AI-Driven Messaging & Insights.” Volume. 10 Issue.4, April-2025 International Journal of Innovative Science and Research Technology (IJISRT), 328-338, https://doi.org/10.38124/ijisrt/25apr027
- L. Chen, P. Chen and Z. Lin, "Artificial Intelligence in Education: A Review," in IEEE Access, vol. 8, pp. 75264-75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
- Winkler, Rainer & Söllner, Matthias. (2018). Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Proceedings. 2018. 15903. 10.5465/AMBPP.2018.15903abstract.
- Mithun Shivakoti and Narsaiah Shivakoti. Role of Artificial Intelligence in Personalized Learning at Higher Education Institutions. International Journal of Contemporary Research in Multidisciplinary. 2023: 2(6):89-92
- Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation. Proceedings of the 40th Annual Meeting on ACL, 311–318. https://doi.org/10.3115/1073083.1073135
- Salvatore Giorgi, Kelsey Isman, Tingting Liu, Zachary Fried, João Sedoc, Brenda Curtis. (2024). Evaluating generative AI responses to real-world drug-related questions. https://doi.org/10.1016/j.psychres.2024.116058
- Ghorashi, Nima & Ismail, Ahmed & Ghosh, Pritha & Sidawy, Anton & Javan, Ramin. (2023). AI-Powered Chatbots in Medical Education: Potential Applications and Implications. Cureus. 15. 10.7759/cureus.43271.
- Chan Kai Siang & Zary Nabil. (2019). Applications and Challenges of Implementing Artificial Intelligence in Medical and Healthcare Education: An Integrative Review (Preprint). JMIR Medical Education. 5. 10.2196/13930.
- Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976–980. https://doi.org/10.1080/0142159X.2019.1595557
- Kumar, Jeya Amantha. (2021). Educational chatbots for project-based learning: investigating learning outcomes for a team-based design course. RUSC. Universities and Knowledge Society Journal. 18. 10.1186/s41239-021-00302-w.
- Cevasco, K.E., Morrison Brown, R.E., Woldeselassie, R. et al. Patient Engagement with Conversational Agents in Health Applications 2016–2022: A Systematic Review and Meta-Analysis. J Med Syst 48, 40 (2024). https://doi.org/10.1007/s10916-024-02059-x
- Milne-Ives, Madison & de Cock, Caroline & Lim, Ernest & Shehadeh, Melissa & Pennington, Nick & Mole, Guy & Meinert, Edward. (2020). The effectiveness of artificial intelligence conversational agents in healthcare: a systematic review (Preprint). Journal of Medical Internet Research. 22. 10.2196/20346.
The pharmaceutical industry uses traditional training and evaluation approaches that often lack personalized
learning, interactive training, and immediate performance assessment thus restricting their effectiveness in today’s fast-
paced educational environments. The Solution implements an AI-based chatbot system that supports pharmaceutical
training along with evaluation needs. The chatbot functions as an interactive learning assistant which enables students and
trainees to participate in real-time dialogues while accessing pharma-related assignments along with quizzes that deliver
instant personalized feedback for each user. It leverages a fine-tuned large language model LLaMA 2 which delivers context-
specific accurate results for pharmaceutical inquiries including both unstructured questions and structured learning
content. The system features three operational modes which enable users to interact through domain-specific questions i.e.
Interactive Question & Answers (1), complete multiple-choice quizzes with automatic evaluation and scoring i.e. Quiz Mode
(2) and generate questions for deeper learning and assessment. i.e. Assignment & Training Questionnaire Generator (3). To
evaluate the solution effectiveness, Available open-source datasets used to fine-tune LLaMA model. The chatbot's
performance was assessed through qualitative assessment and its ability to accurately interpret input and generate output.
The AI solution delivers simplified knowledge distribution and assessment during remote training sessions while
simultaneously reducing time requirements for evaluation tasks. Future improvements to this system might include support
for multiple languages as well as real-time analytics integration and learning paths. The chatbot system provides users with
assessment tools and evaluation criteria allowing users to monitor their learning growth at any time from any location. The
system includes interactive training tools such as quizzes together with questionnaires and assignment prompts to get
continuous user participation while reinforcing their knowledge acquisition. Through this intelligent system, the model not
only delivers informative responses but also encourages self-assessment, making it a valuable tool for modern
pharmaceutical training and education.
Keywords :
Pharmaceutical Training, AI-Based Chatbot, Personalized Feedback, Interactive Learning, LLaMA 2 (Large Language Model Meta AI), Real-Time Assessment, Knowledge Distribution, Questionnaire Generator.