Optimizing an Automatic Text Analysis Model with Generative Artificial Intelligence System for Gaming Strategic Behaviors

Authors

  • GengDe Hong National Central University
  • Ju-Ling Shih Graduate Institute of Network Learning Technology
  • George Ghinea Department of Computer Science
  • Yu-Hao Lu Graduate Institute of Network Learning Technology
  • Hsuan-Wen Chen Graduate Institute of Network Learning Technology

DOI:

https://doi.org/10.52731/liir.v005.235

Keywords:

Generative AI, ChatGPT, Text Analysis, Gaming Strategic Behaivors

Abstract

ChatGPT, as an important system of Generative Artificial Intelligence (GenAI), has had a profound impact on various fields, and has the potential for processing textual dialogues regarding gaming behavior analysis. Therefore, this study aims to build an automatic text analysis model for gaming strategic behaviors by enhancing its objectivity and efficiency. Three stages of tests were conducted to optimize its function for achieving comprehensive understanding of using GenAI. Behavior coding with clear definition, gaming contexts, and analysis goals are all important factors. Through the analysis model, we can ensure the capability of the analysis accuracy. By using GenAI, researchers significantly reduce the time and cost associated with manual analysis that enables the process of large volumes of textual dialogue data.

References

Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22.

Dowling, M., & Lucey, B. (2023). ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53, 103662.

MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022, August). Generating diverse code explanations using the gpt-3 large language model. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 2 (pp. 37-39).

Sallam, M., Salim, N. A., Barakat, M., & Ala'a, B. (2023). ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations. Narra J, 3(1).

Dai, W., Lin, J., Jin, H., Li, T., Tsai, Y. S., Gašević, D., & Chen, G. (2023, July). Can large language models provide feedback to students? A case study on ChatGPT. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 323-325). IEEE.

Shih, J. L., Chiu, M. M., & Lin, C. H. (2022). Personalities, sequences of strategies and actions, and game attacks: A statistical discourse analysis of strategic board game play. Computers in Human Behavior, 133, 107271.

Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.

Zagal, J. P., Tomuro, N., & Shepitsen, A. (2012). Natural language processing in game studies research: An overview. Simulation & Gaming, 43(3), 356-373.

Pacella, D., & Marocco, D. (2022). Understanding Negotiation: A Text-Mining and NLP Approach to Virtual Interactions in a Simulation Game. Applied Sciences, 12(10), 5243.

Kaddari, Z., Mellah, Y., Berrich, J., Belkasmi, M. G., & Bouchentouf, T. (2020, March). Natural language processing: Challenges and future directions. In International Conference on Artificial Intelligence & Industrial Applications (pp. 236-246). Cham: Springer International Publishing.

Kasneci, E.; Seßler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274.

Hong, G. D., Shih, J. L., & Lu, Y. H. (2022). High-level Cooperative Behavior Model of Online Summit Games. The Main Proceedings of the 30th International Conference on Computers in Education (530-535), Malaysia: Asia-Pacific Society on Computers in Education.

Hong, G. D., Shih, J. L., & Lu, Y. H. (2022). Development and Evaluation of Online Issue Game System. The Proceedings of the 26th Global Chinese Conference on Computers in Education (230-237). Beijing.

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Published

2024-09-15