Prompt Engineering with ChatGPT in the academic context of HCI: a rapid literature review
Abstract
The field of Artificial Intelligence (AI) advances every day and is reflecting in different areas of knowledge, including the academic context of HCI (Human-Computer Interaction). However, there is indeed an issue with the use of such tools and their inherent risks. Through a systematic literature review, we identified effective methods for formulating prompts that improve the accuracy and effectiveness of responses generated by ChatGPT. The research reveals that prompt engineering not only enhances interaction with AI but also significantly contributes to reducing subjectivity in software development estimates and HCI practices.References
Alotaib, L., Seher, S., and Janković, D. (2024). Cyberattacks using chatgpt: Exploring malicious content generation through prompt engineering.
Cartaxo, B., Pinto, G., and Soares, S. (2018). The role of rapid reviews in supporting decision-making in software engineering practice. In International Conference on Evaluation and Assessment in Software Engineering, pages 24–34, New York. Association for Computing Machinery.
cheng Lei, W. et al. (2023). Using chatgpt on improving program performance with pprof and benchmark.
Dehouche, N. (2021). Plagiarism in the age of massive generative pre-trained transformers (gpt-3). Ethics in Science and Environmental Politics, 21:17–23.
Ekin, S. (2023). Prompt engineering for chatgpt: a quick guide to techniques, tips, and best practices. TechRxiv.
Gamage, G. et al. (2023). Augmenting industrial chatbots in energy systems using chatgpt generative ai.
Gibbs, G. (2009). Análise de dados qualitativos. Artmed, Porto Alegre.
Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Ann Biomed Eng, 51:2629–2633.
Hui, A., Shankararaman, V., and Lieh, E. Vision paper advancing of ai explainability for the use of chatgpt in government agencies - proposal of a 4-step framework. Journal Name.
Kamnis, S. (2023). Generative pre-trained transformers (gpt) for surface engineering. Surface and Coatings Technology, 466:129680.
Panagoulias, D. et al. (2023). Evaluating the potential of llms and chatgpt on medical diagnosis and treatment.
Panagoulias, D. et al. (2024). Evaluation of chatgpt-supported diagnosis, staging and treatment planning for the case of lung cancer.
Spasić, A. and Janković, D. Using chatgpt standard prompt engineering techniques in lesson preparation role - instructions and seed-word prompts. Journal Name.
Utaliyeva, A. et al. (2023). Chatgpt: A threat to spam filtering systems.
Wang, M. et al. (2024). Unleashing chatgpt’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering.
Zhou, M. et al. (2023). Application of large language models in professional fields.
Cartaxo, B., Pinto, G., and Soares, S. (2018). The role of rapid reviews in supporting decision-making in software engineering practice. In International Conference on Evaluation and Assessment in Software Engineering, pages 24–34, New York. Association for Computing Machinery.
cheng Lei, W. et al. (2023). Using chatgpt on improving program performance with pprof and benchmark.
Dehouche, N. (2021). Plagiarism in the age of massive generative pre-trained transformers (gpt-3). Ethics in Science and Environmental Politics, 21:17–23.
Ekin, S. (2023). Prompt engineering for chatgpt: a quick guide to techniques, tips, and best practices. TechRxiv.
Gamage, G. et al. (2023). Augmenting industrial chatbots in energy systems using chatgpt generative ai.
Gibbs, G. (2009). Análise de dados qualitativos. Artmed, Porto Alegre.
Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Ann Biomed Eng, 51:2629–2633.
Hui, A., Shankararaman, V., and Lieh, E. Vision paper advancing of ai explainability for the use of chatgpt in government agencies - proposal of a 4-step framework. Journal Name.
Kamnis, S. (2023). Generative pre-trained transformers (gpt) for surface engineering. Surface and Coatings Technology, 466:129680.
Panagoulias, D. et al. (2023). Evaluating the potential of llms and chatgpt on medical diagnosis and treatment.
Panagoulias, D. et al. (2024). Evaluation of chatgpt-supported diagnosis, staging and treatment planning for the case of lung cancer.
Spasić, A. and Janković, D. Using chatgpt standard prompt engineering techniques in lesson preparation role - instructions and seed-word prompts. Journal Name.
Utaliyeva, A. et al. (2023). Chatgpt: A threat to spam filtering systems.
Wang, M. et al. (2024). Unleashing chatgpt’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering.
Zhou, M. et al. (2023). Application of large language models in professional fields.
Published
2024-10-07
How to Cite
SANTOS, Gabriel; MARTINS, João; EVANGELISTA, Gessé.
Prompt Engineering with ChatGPT in the academic context of HCI: a rapid literature review. In: POSTERS & DEMONSTRATIONS - BRAZILIAN SYMPOSIUM ON HUMAN FACTORS IN COMPUTATIONAL SYSTEMS (IHC), 23. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 144-148.
DOI: https://doi.org/10.5753/ihc_estendido.2024.243968.
