GenAI Embedded in Activities of Academic Research: Experiences and Lessons from HCI Studies
Resumo
Introduction: Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, are increasingly being used to support various stages of scientific research. Objective: This paper aims to report on nine specific instances of the author team’s experience using ChatGPT to support their research activities within the field of Human-Computer Interaction (HCI). The goal is to identify and present lessons learned from these applications. Methodology: The methodology involved documenting and analyzing nine distinct cases where the author team utilized ChatGPT to assist with different research tasks. The analysis focused on identifying patterns of effective use and drawing conclusions regarding the tool’s strengths and limitations. Results: The analysis of these nine cases yielded six key lessons, demonstrating that ChatGPT provides substantial support across several research tasks. These tasks include content refinement, text reduction, insight generation, screening, summarization, and data visualization. The primary benefits observed were accelerated workfows, improved clarity, and enhanced systematic organization. However, the study also revealed that the effectiveness of ChatGPT is highly dependent on the use of precise, well-structured prompts and the segmentation of complex tasks into smaller, manageable units.
Palavras-chave:
GenAI, ChatGPT, UX Research
Referências
Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A. E., e Zayed, T. (2023). Harnessing the power of chatgpt for automating systematic review process: methodology, case study, limitations, and future directions. Systems, 11(7):351.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., e Corvello, V. (2023). On the use of ai-based tools like chatgpt to support management research. European Journal of Innovation Management, 26(7):233–241.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024). A survey on evaluation of large language models. ACM transactions on intelligent systems and technology, 15(3):1–45.
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications.
Chen, C., Lee, S., Jang, E., e Sundar, S. S. (2024). Is your prompt detailed enough? exploring the effects of prompt coaching on users’ perceptions, engagement, and trust in text-to-image generative ai tools. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems, pages 1–12.
Dong, M. M., Stratopoulos, T. C., e Wang, V. X. (2024). A scoping review of chatgpt research in accounting and finance. International Journal of Accounting Information Systems, 55:100715.
Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., Fiedorowicz, J. G., Smith, A. L., e Solmi, M. (2024). How to optimize the systematic review process using ai tools. JCPP advances, 4(2):e12234.
Felizardo, K. R., Lima, M. S., Deizepe, A., Conte, T. U., e Steinmacher, I. (2024). Chatgpt application in systematic literature reviews in software engineering: an evaluation of its accuracy to support the selection activity. In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pages 25–36.
Fleiss, J. L., Levin, B., e Paik, M. C. (2003). Statistical Methods for Rates and Proportions. John Wiley & Sons, Inc, 3rd edn. edition.
Freire, A. P., Cardoso, P. C. F., e Salgado, A. d. L. (2023). May we consult chatgpt in our human-computer interaction written exam? an experience report after a professor answered yes. In Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems, pages 1–11.
Gibbs, G. (2018). Analyzing Qualitative Data. SAGE Publications Ltd, 2 edition.
Gilardi, F., Alizadeh, M., e Kubli, M. (2023). Chatgpt outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30):e2305016120.
Hanaf, A. M., Al-mansi, M. M., Al-Sharif, O. A., et al. (2025). Generative ai in academia: A comprehensive review of applications and implications for the research process. International Journal of Engineering and Applied Sciences-October 6 University, 2(1):91–110.
Hitch, D. (2024). Artifcial intelligence augmented qualitative analysis: the way of the future? Qualitative Health Research, 34(7):595–606.
Hokkanen, L. e Väänänen-Vainio-Mattila, K. (2015). Ux work in startups: Current practices and future needs. In Lassenius, C., Dingsøyr, T., e Paasivaara, M., editors, Agile Processes in Software Engineering and Extreme Programming, pages 81–92, Cham. Springer International Publishing.
Hosseini, M. e Horbach, S. P. (2023). Fighting reviewer fatigue or amplifying bias? considerations and recommendations for use of chatgpt and other large language models in scholarly peer review. Research integrity and peer review, 8(1):4.
Khalifa, M. e Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, page 100145.
Lazar, J., Feng, J. H., e Hochheiser, H. (2017). Research Methods in Human-Computer Interaction. Morgan Kaufmann, 2nd edn. edition.
Lefebvre, C., Manheimer, E., e Glanville, J. (2008). Searching for studies. Cochrane handbook for systematic reviews of interventions: Cochrane book series, pages 95–150.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., e Moher, D. (2009). The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Bmj, 339.
Liu, V. e Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI conference on human factors in computing systems, pages 1–23.
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., e Wang, Z. (2023). Chatgpt and a new academic reality: Artifcial intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5):570–581.
Martinelli, S., Choma, J., e Zaina, L. (2023). Análise Qualitativa em IHC: da codifcação à criação de visualizações. In Minicursos do XXII Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, pages 24–45, Porto Alegre, RS, Brasil. SBC.
Perkins, M. e Roe, J. (2024). Generative ai tools in academic research: Applications and implications for qualitative and quantitative research methodologies. arXiv preprint arXiv:2408.06872.
Petersen, K. e Gerken, J. M. (2025). On the road to interactive llm-based systematic mapping studies. Information and Software Technology, 178:107611.
Rahman, M. M., Terano, H. J., Rahman, M. N., Salamzadeh, A., e Rahaman, M. S. (2023). Chatgpt and academic research: A review and recommendations based on practical examples. Rahman, M., Terano, HJR, Rahman, N., Salamzadeh, A., Rahaman, S.(2023). ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples. Journal of Education, Management and Development Studies, 3(1):1–12.
Schmidt, D. C., Spencer-Smith, J., Fu, Q., e White, J. (2024). Towards a catalog of prompt patterns to enhance the discipline of prompt engineering. ACM SIGAda Ada Letters, 43(2):43–51.
Tenhundfeld, N. L. (2023). Two birds with one stone: Writing a paper entitled “chatgpt as a tool for studying human-ai interaction in the wild” with chatgpt. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 67, pages 2007–2012. SAGE Publications Sage CA: Los Angeles, CA.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., e Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Watkins, R. (2024). Guidance for researchers and peer-reviewers on the ethical use of large language models (llms) in scientific research workfows. AI and Ethics, 4(4):969–974.
Wen, J. e Wang, W. (2023). The future of chatgpt in academic research and publishing: A commentary for clinical and translational medicine. Clinical and Translational Medicine, 13(3):e1207.
Xiao, Z., Yuan, X., Liao, Q. V., Abdelghani, R., e Oudeyer, P.-Y. (2023). Supporting qualitative analysis with large language models: Combining codebook with gpt-3 for deductive coding. In Companion proceedings of the 28th international conference on intelligent user interfaces, pages 75–78.
Zaina, L., Prates, R. O., Delabrida Silva, S. E., Choma, J., Valentim, N. M. C., Frigo, L. B., e Bicho, A. D. L. (2024). Grandihc-br 2025-2035-gc7: Interaction with emerging technologies: An ecosystem integrating humans technologies and contexts. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–21.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., e Corvello, V. (2023). On the use of ai-based tools like chatgpt to support management research. European Journal of Innovation Management, 26(7):233–241.
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024). A survey on evaluation of large language models. ACM transactions on intelligent systems and technology, 15(3):1–45.
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications.
Chen, C., Lee, S., Jang, E., e Sundar, S. S. (2024). Is your prompt detailed enough? exploring the effects of prompt coaching on users’ perceptions, engagement, and trust in text-to-image generative ai tools. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems, pages 1–12.
Dong, M. M., Stratopoulos, T. C., e Wang, V. X. (2024). A scoping review of chatgpt research in accounting and finance. International Journal of Accounting Information Systems, 55:100715.
Fabiano, N., Gupta, A., Bhambra, N., Luu, B., Wong, S., Maaz, M., Fiedorowicz, J. G., Smith, A. L., e Solmi, M. (2024). How to optimize the systematic review process using ai tools. JCPP advances, 4(2):e12234.
Felizardo, K. R., Lima, M. S., Deizepe, A., Conte, T. U., e Steinmacher, I. (2024). Chatgpt application in systematic literature reviews in software engineering: an evaluation of its accuracy to support the selection activity. In Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pages 25–36.
Fleiss, J. L., Levin, B., e Paik, M. C. (2003). Statistical Methods for Rates and Proportions. John Wiley & Sons, Inc, 3rd edn. edition.
Freire, A. P., Cardoso, P. C. F., e Salgado, A. d. L. (2023). May we consult chatgpt in our human-computer interaction written exam? an experience report after a professor answered yes. In Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems, pages 1–11.
Gibbs, G. (2018). Analyzing Qualitative Data. SAGE Publications Ltd, 2 edition.
Gilardi, F., Alizadeh, M., e Kubli, M. (2023). Chatgpt outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30):e2305016120.
Hanaf, A. M., Al-mansi, M. M., Al-Sharif, O. A., et al. (2025). Generative ai in academia: A comprehensive review of applications and implications for the research process. International Journal of Engineering and Applied Sciences-October 6 University, 2(1):91–110.
Hitch, D. (2024). Artifcial intelligence augmented qualitative analysis: the way of the future? Qualitative Health Research, 34(7):595–606.
Hokkanen, L. e Väänänen-Vainio-Mattila, K. (2015). Ux work in startups: Current practices and future needs. In Lassenius, C., Dingsøyr, T., e Paasivaara, M., editors, Agile Processes in Software Engineering and Extreme Programming, pages 81–92, Cham. Springer International Publishing.
Hosseini, M. e Horbach, S. P. (2023). Fighting reviewer fatigue or amplifying bias? considerations and recommendations for use of chatgpt and other large language models in scholarly peer review. Research integrity and peer review, 8(1):4.
Khalifa, M. e Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, page 100145.
Lazar, J., Feng, J. H., e Hochheiser, H. (2017). Research Methods in Human-Computer Interaction. Morgan Kaufmann, 2nd edn. edition.
Lefebvre, C., Manheimer, E., e Glanville, J. (2008). Searching for studies. Cochrane handbook for systematic reviews of interventions: Cochrane book series, pages 95–150.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., e Moher, D. (2009). The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Bmj, 339.
Liu, V. e Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI conference on human factors in computing systems, pages 1–23.
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., e Wang, Z. (2023). Chatgpt and a new academic reality: Artifcial intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5):570–581.
Martinelli, S., Choma, J., e Zaina, L. (2023). Análise Qualitativa em IHC: da codifcação à criação de visualizações. In Minicursos do XXII Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais, pages 24–45, Porto Alegre, RS, Brasil. SBC.
Perkins, M. e Roe, J. (2024). Generative ai tools in academic research: Applications and implications for qualitative and quantitative research methodologies. arXiv preprint arXiv:2408.06872.
Petersen, K. e Gerken, J. M. (2025). On the road to interactive llm-based systematic mapping studies. Information and Software Technology, 178:107611.
Rahman, M. M., Terano, H. J., Rahman, M. N., Salamzadeh, A., e Rahaman, M. S. (2023). Chatgpt and academic research: A review and recommendations based on practical examples. Rahman, M., Terano, HJR, Rahman, N., Salamzadeh, A., Rahaman, S.(2023). ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples. Journal of Education, Management and Development Studies, 3(1):1–12.
Schmidt, D. C., Spencer-Smith, J., Fu, Q., e White, J. (2024). Towards a catalog of prompt patterns to enhance the discipline of prompt engineering. ACM SIGAda Ada Letters, 43(2):43–51.
Tenhundfeld, N. L. (2023). Two birds with one stone: Writing a paper entitled “chatgpt as a tool for studying human-ai interaction in the wild” with chatgpt. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 67, pages 2007–2012. SAGE Publications Sage CA: Los Angeles, CA.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., e Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Watkins, R. (2024). Guidance for researchers and peer-reviewers on the ethical use of large language models (llms) in scientific research workfows. AI and Ethics, 4(4):969–974.
Wen, J. e Wang, W. (2023). The future of chatgpt in academic research and publishing: A commentary for clinical and translational medicine. Clinical and Translational Medicine, 13(3):e1207.
Xiao, Z., Yuan, X., Liao, Q. V., Abdelghani, R., e Oudeyer, P.-Y. (2023). Supporting qualitative analysis with large language models: Combining codebook with gpt-3 for deductive coding. In Companion proceedings of the 28th international conference on intelligent user interfaces, pages 75–78.
Zaina, L., Prates, R. O., Delabrida Silva, S. E., Choma, J., Valentim, N. M. C., Frigo, L. B., e Bicho, A. D. L. (2024). Grandihc-br 2025-2035-gc7: Interaction with emerging technologies: An ecosystem integrating humans technologies and contexts. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–21.
Publicado
08/09/2025
Como Citar
ALENCAR, Tatiana; CISCON, Leonardo; PEREIRA, Gessé; MACEDO, Maylon; MARTINELLI, Suéllen; CHOMA, Joelma; ZAINA, Luciana.
GenAI Embedded in Activities of Academic Research: Experiences and Lessons from HCI Studies. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 58-79.
DOI: https://doi.org/10.5753/ihc.2025.10800.
