Conversational AI in Social Study Surveys: A Comprehensive Review: Evaluating the efficacy, user acceptance, and potential biases of chatbot-driven surveys in social research.

References

[1] Adewumi, T. P., Liwicki, F., & Liwicki, M. (2022). State-of-the-art in Open-domain Conversational AI: A Survey. https://dx.doi.org/10.48550/arXiv.2205.00965 DOI: 10.48550/arXiv.2205.00965

[2] Adus, S., Macklin, J., & Pinto, A. (2023). Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. BMC Health Services Research, 23, 1163. https://dx.doi.org/10.1186/s12913-023-10098-2

[3] Ashktorab, Z., Dugan, C., Johnson, J., Pan, Q., Zhang, W., Kumaravel, S., & Campbell, M. (2021,). Effects of communication directionality and AI agent differences in human-AI interaction. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-15. https://dx.doi.org/10.1145/3411764.3445256

[4] Au, J., Falloon, C., Ravi, A., Ha, P., & Le, S. (2023). A Beta-Prototype Chatbot for Increasing Health Literacy of Patients with Decompensated Cirrhosis: Usability Study. JMIR Human Factors, 10(1), e42506. https://dx.doi.org/10.2196/42506

[5] Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Using Chatbots as AI Conversational Partners in Language Learning. Applied Sciences, 12(17), 8427. https://dx.doi.org/10.3390/app12178427

[6] Birhane, A., Ruane, E., Laurent, T., S. Brown, M., Flowers, J., Ventresque, A., & L. Dancy, C. (2022). The forgotten margins of AI ethics. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 948-958. DOI: 10.1145/3531146.3533157

[7] Borsci, S., Malizia, A., Schmettow, M., Van Der Velde, F., Tariverdiyeva, G., Balaji, D., & Chamberlain, A. (2022). The Chatbot Usability Scale: the design and pilot of a usability scale for interaction with AI-based conversational agents. Personal and Ubiquitous Computing, 26, 95-119. https://dx.doi.org/10.1007/s00779-021-01582-9

[8] Chakriswaran, P., Vincent, D., Srinivasan, K., Sharma, V., Chang, C.-Y., & Reina, D. (2019). Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues. Applied Sciences, 9(24), 5462. DOI: 10.3390/app9245462

[9] Chen, K., Shao, A., Burapacheep, J., & Li, Y. (2024). Conversational AI and equity through assessing GPT-3’s communication with diverse social groups on contentious topics. Scientific Reports, 14(1), 1561. https://dx.doi.org/10.1038/s41598-024-51969-w

[10] Chew, H. S. J. (2022). The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations. JMIR Medical Informatics, 10(4), e32578. https://dx.doi.org/10.2196/32578 DOI: 10.2196/32578

[11] Chung, D., Kim, H., & Ahn, S. (2022). An integrated study of user acceptance and resistance on voice commerce. International Journal of Innovation and Technology Management, 19(07), 2250025. https://dx.doi.org/10.1142/s0219877022500250

[12] Enticott, G., Earl, L., & Gates, M. C. (2022). A systematic review of social research data collection methods used to investigate voluntary animal disease reporting behaviour. Transboundary and Emerging Diseases, 69(5), 2573-2587. https://dx.doi.org/10.1111/tbed.14407

[13] Feher, K., & Veres, Z. (2022). Trends, risks and potential cooperations in the AI development market: expectations of the Hungarian investors and developers in an international context. International Journal of Sociology and Social Policy, 43(1/2), 107-125. https://dx.doi.org/10.1108/ijssp-08-2021-0205

[14] Gao, J., Galley, M., & Li, L. (2018, June). Neural approaches to conversational AI. In The 41st international ACM SIGIR conference on research & development in information retrieval, pp. 1371-1374. DOI: 10.18653/v1/P18-5002

[15] Garg, M. (2023). Mental health analysis in social media posts: a survey. Archives of Computational Methods in Engineering, 30(3), 1819-1842. https://dx.doi.org/10.1007/s11831-022-09863-z

[16] Hidayat-ur-Rehman, I., & Ibrahim, Y. (2023). Exploring factors influencing educators’ adoption of ChatGPT: A mixed method approach. Information Technology & People. Vol. ahead-of-print No. ahead-of-print. DOI: 10.1108/itse-07-2023-0127.

[17] Kasi, M., & Shaheen, R. (2020). How Will Information Services of Social Media Portals Change in the Future When Artificial Intelligence is implemented?. European Journal of Technology, 4(1), 31-46. https://dx.doi.org/10.47672/ejt.631

[18] Kaywan, P., Ahmed, K., Ibaida, A., Miao, Y., & Gu, B. (2023). Early detection of depression using a conversational AI bot: A non-clinical trial. PLOS ONE, 18(2), e0279743. https://dx.doi.org/10.1371/journal.pone.0279743

[19] Lee, S., Nah, S., Chung, D. S., & Kim, J. (2020). Predicting ai news credibility: communicative or social capital or both?. Communication Studies, 71(3), 428-447. https://dx.doi.org/10.1080/10510974.2020.1779769

[20] Lekova, A., Tsvetkova, P., & Andreeva, A. (2023, September). System Software Architecture for Enhancing Human-robot Interaction by Conversational AI," 2023 International Conference on Information Technologies (InfoTech), Varna, Bulgaria, 2023, pp. 1-6, DOI: 10.1109/InfoTech58664.2023.10266870

[21] Li, T., Vorvoreanu, M., DeBellis, D., & Amershi, S. (2023). Assessing human-ai interaction early through factorial surveys: A study on the guidelines for human-ai interaction. ACM Transactions on Computer-Human Interaction, 30(5), 1-45. https://dx.doi.org/10.1145/3511605.

[22] Lim, J. S., Choi, J. H., & Lee, H. M. (2020). Measuring the perceived anthropomorphism of an AI conversational agent: scale development and validation. Korean Society for Journalism and Communication Studies, 64(4), 436-470. https://dx.doi.org/10.20879/kjjcs.2020.64.4.012

[23] Mahdavi, A., Amanzadeh, M., Hamedan, M., & Naemi, R. (2023). Artificial Intelligence-Based Chatbots to Combat COVID-19 Pandemic: A Scoping Review. Research Square. https://dx.doi.org/10.21203/rs.3.rs-2565141/v1 DOI: 10.21203/rs.3.rs-2565141/v1

[24] Ng, Y. (2022). Exploring the association between use of conversational artificial intelligence and social capital: Survey evidence from Hong Kong. New Media & Society. https://dx.doi.org/10.1177/14614448221074047.

[25] Puaschunder, J. M. (2019). Stakeholder Perspectives on Artificial Intelligence (AI), Robotics and Big Data in Healthcare: An Empirical Study (December 3, 2019). Available at SSRN: https://ssrn.com/abstract=3497261 or http://dx.doi.org/10.2139/ssrn.3497261.

[26] Rahmat, F. N., Rusdin, F. R., & Nofiasari, W. (2023). Metaverse Journalism: Opportunities and Challenges for the Media Industry in Indonesia. International Journal of Science and Society, 5(4), 207-218. https://dx.doi.org/10.54783/ijsoc.v5i4.779

[27] Rajagopal, M., Sivasakthivel, R., Ramar, G., Mansurali, A., & Karuppasamy, S. K. (2023, October). A Framework for AI Governance in Public Administration – A Smart Governance Perspective," 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 488-495, https://dx.doi.org/10.1109/I-SMAC58438.2023.10290366.

[28] Shahbazi, N., Lin, Y., Asudeh, A., & Jagadish, H. (2022). Representation Bias in Data: A Survey on Identification and Resolution Techniques. ACM Computing Surveys, 55(293), 1-39. https://dx.doi.org/10.1145/3588433 DOI: 10.1145/3588433.

[29] Siahaan, B. P., & Prasetio, E. A. (2022). Understanding Customer Insights Through Big Data: Innovations in Brand Evaluation in the Automotive Industry. The Asian Journal of Technology Management, 15(1), 51-68. https://dx.doi.org/10.12695/ajtm.2022.15.1.4.

[30] Simeoni, R., Colonnelli, F., Eutizi, V., Marchetti, M., Paolini, E., Papalini, V., Punturo, A., Salvò, A., Scipinotti, N., Serpente, C., Barbini, E., Troscia, R., Maccioni, G., & Giansanti, D. (2021). The Social Robot and the Digital Physiotherapist: Are We Ready for the Team Play? Healthcare, 9(11), 1454. DOI: 10.3390/healthcare9111454

[31] Suhluli, S. A., & Ali Khan, S. M. F. (2022). Determinants of user acceptance of wearable IoT devices. Cogent Engineering, 9(1), 2087456.https://dx.doi.org/10.1080/23311916.2022.2087456

[32] Toader, D., Boca, G., Toader, R., Macelaru, M., Toader, C., Ighian, D., & Rădulescu, A. (2019). The Effect of Social Presence and Chatbot Errors on Trust. Sustainability, 12(1), 256. DOI: 10.3390/su12010256.

[33] Venkit, P. N. (2023, August). Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, pp. 1004-1005. https://dx.doi.org/10.1145/3600211.3604754

[34] Willie, M. M., & Nkomo, P. (2019). Digital transformation in healthcare–South Africa context. Global Journal of Immunology and Allergic Diseases, 7(1), 1-5. https://dx.doi.org/10.31907/2310-6980.2019.07.01

[35] Xiong, Y., Shi, Y., Pu, Q., & Liu, N. (2023). More trust or more risk? User acceptance of artificial intelligence virtual assistant. Human Factors and Ergonomics in Manufacturing & Service Industries. 10.1002/hfm.21020. https://dx.doi.org/10.1002/hfm.21020

[36] Yan, R. (2018). "Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pp. 5520-5526. DOI: 10.24963/ijcai.2018/778.

[37] Yang, L., Ene, I. C., Arabi Belaghi, R., Koff, D., Stein, N., & Santaguida, P. (2022). Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review. European Radiology, 32(3), 1477-1495. https://dx.doi.org/10.1007/s00330-021-08214-z

[38] Zheng, Q., Tang, Y., Liu, Y., Liu, W., & Huang, Y. (2022, April). UX research on conversational human-AI interaction: A literature review of the ACM Digital Library. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1-24. https://dx.doi.org/10.1145/3491102.3501855

@article{1710118,
      author = {Adaobi Beverly Akonobi, Christiana Onyinyechi Okpokwu},
      title = {Conversational AI in Social Study Surveys: A Comprehensive Review: Evaluating the efficacy, user acceptance, and potential biases of chatbot-driven surveys in social research.},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {326-341},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710118.pdf},
      abstract = {This study explores the integration of Conversational Artificial Intelligence (AI) in social study surveys, focusing on its efficacy, user acceptance, potential biases, and implications for stakeholders. Employing a systematic literature review and content analysis, we examined peer-reviewed articles, conference proceedings, and white papers from databases such as PubMed, IEEE Xplore, and Google Scholar, published from 2010 to 2024. Our methodology included defining inclusion and exclusion criteria to ensure a focused examination of the current state and future directions of Conversational AI in social research. Key findings reveal that Conversational AI enhances the efficiency and precision of data collection, offering a more engaging participant experience and the potential for deeper insights into societal trends. However, challenges such as AI-induced biases and concerns over data privacy and security necessitate careful navigation. The study underscores the importance of developing comprehensive ethical guidelines, rigorous testing to mitigate biases, and enhancing user acceptance through intuitive AI interfaces. Strategic recommendations highlight the need for interdisciplinary collaboration to address ethical and methodological challenges, ensuring that Conversational AI's integration into social research enhances rather than compromises study quality and integrity. In conclusion, while Conversational AI presents a promising avenue for revolutionizing social research, embracing these technologies requires a balanced approach that combines caution with confidence, guided by ethical principles and a commitment to addressing biases and fostering user acceptance.},
      keywords = {Conversational Artificial Intelligence, Social Study Surveys, AI-induced Biases Ethical Guidelines.},
      month = {June},
  }