Enhancing user experience and trust in advanced LLM-based conversational agents

Yuanyuan   Xu; Weiting  Gao; Yining  Wang; Xinyang  Shan; Yin-Shan   Lin

doi:10.59400/cai.v2i2.1467

Enhancing user experience and trust in advanced LLM-based conversational agents

Yuanyuan Xu Tongji University, Shanghai 200092, China
Weiting Gao Amazon, Seattle, WA 98121, USA
Yining Wang Bentley University, Waltham, MA 02452, USA
Xinyang Shan Tongji University, Shanghai 200092, China
Yin-Shan Lin Northeastern University, Boston, MA 02115, USA

Ariticle ID: 1467

168 Views, 69 PDF Downloads

DOI: https://doi.org/10.59400/cai.v2i2.1467

Keywords: large language models (LLMs); user experience (UX); conversational agents; transparency; data security

Abstract

This study explores the enhancement of user experience (UX) and trust in advanced Large Language Model (LLM)-based conversational agents such as ChatGPT. The research involves a controlled experiment comparing participants using an LLM interface with those using a traditional messaging app with a human consultant. The results indicate that LLM-based agents offer higher satisfaction and lower cognitive load, demonstrating the potential for LLMs to revolutionize various applications from customer service to healthcare consultancy and shopping assistance. Despite these positive findings, the study also highlights significant concerns regarding transparency and data security. Participants expressed a need for clearer understanding of how LLMs process information and make decisions. The perceived opacity of these processes can hinder user trust, especially in sensitive applications such as healthcare. Additionally, robust data protection measures are crucial to ensure user privacy and foster trust in these systems. To address these issues, future research and development should focus on enhancing the transparency of LLM operations and strengthening data security protocols. Providing users with clear explanations of how their data is used and how decisions are made can build greater trust. Moreover, specialized applications may require tailored solutions to meet specific user expectations and regulatory requirements. In conclusion, while LLM-based conversational agents have demonstrated substantial advantages in improving user experience, addressing transparency and security concerns is essential for their broader acceptance and effective deployment. By focusing on these areas, developers can create more trustworthy and user-friendly AI systems, paving the way for their integration into diverse fields and everyday use.

References

[1] Zhuang Y, Yu Y, Wang K, et al. Toolqa: A dataset for llm question answering with external tools. Adv Neural Inf Process Syst. 2024; 36.

[2] Panda S, Kaur N. Exploring the viability of ChatGPT as an alternative to traditional chatbot systems in library and information centers. Library Hi Tech News. 2023; 40(3): 22-25. doi: 10.1108/lhtn-02-2023-0032

[3] Valtolina S, Barricelli BR, Di Gaetano S. Communicability of traditional interfaces VS chatbots in healthcare and smart home domains. Behaviour & Information Technology. 2019; 39(1): 108-132. doi: 10.1080/0144929x.2019.1637025

[4] Stoeckli E, Dremel C, Uebernickel F, et al. How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electronic Markets. 2019; 30(2): 369-403. doi: 10.1007/s12525-019-00359-6

[5] Topsakal O, Akinci TC. Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast. International Conference on Applied Engineering and Natural Sciences. 2023; 1(1): 1050-1056. doi: 10.59287/icaens.1127

[6] Yao Y, Duan J, Xu K, et al. A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly. High-Confidence Computing. 2024; 4(2): 100211. doi: 10.1016/j.hcc.2024.100211

[7] Allouch M, Azaria A, Azoulay R. Conversational Agents: Goals, Technologies, Vision and Challenges. Sensors. 2021; 21(24): 8448. doi: 10.3390/s21248448

[8] Wahde M, Virgolin M. Conversational agents: Theory and applications. World Scientific Publishing Company. 2022: 497-544.

[9] Moore RJ, Szymanski MH, Arar R, et al. Studies in Conversational UX Design. Springer International Publishing; 2018. doi: 10.1007/978-3-319-95579-7

[10] Yang X, Aurisicchio M, Baxter W. Understanding Affective Experiences with Conversational Agents. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. doi: 10.1145/3290605.3300772

[11] Moore RJ, Arar R, Ren GJ, et al. Conversational UX Design. In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. doi: 10.1145/3027063.3027077

[12] Kim CY, Lee CP, Mutlu B. Understanding Large-Language Model (LLM)-powered Human-Robot Interaction. In: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. pp. 371-380. doi: 10.1145/3610977.3634966

[13] Abbasiantaeb Z, Yuan Y, Kanoulas E, et al. Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining; 2024. doi: 10.1145/3616855.3635856

[14] Motta I, Quaresma M. Increasing Transparency to Design Inclusive Conversational Agents (CAs): Perspectives and Open Issues. In: Proceedings of the 5th International Conference on Conversational User Interfaces; 2023. pp. 1-4. doi: 10.1145/3571884.3604304

[15] Hasal M, Nowaková J, Ahmed Saghair K, et al. Chatbots: Security, privacy, data protection, and social aspects. Concurrency and Computation: Practice and Experience. 2021; 33(19). doi: 10.1002/cpe.6426

[16] Stieglitz S, Hofeditz L, Brünker F, et al. Design principles for conversational agents to support Emergency Management Agencies. International Journal of Information Management. 2022; 63: 102469. doi: 10.1016/j.ijinfomgt.2021.102469

[17] Van Brummelen J, Kelleher M, Tian MC, et al. What Do Children and Parents Want and Perceive in Conversational Agents? Towards Transparent, Trustworthy, Democratized Agents. In: Proceedings of the 22nd Annual ACM Interaction Design and Children Conference. doi: 10.1145/3585088.3589353

[18] Rosruen N, Samanchuen T. Chatbot Utilization for Medical Consultant System. In: Proceedings of the 2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON). doi: 10.1109/times-icon.2018.8621678

[19] Godse NA, Deodhar S, Raut S, et al. Implementation of Chatbot for ITSM Application Using IBM Watson. In: Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). doi: 10.1109/iccubea.2018.8697411

[20] Rohman MA, Subarkah P. Design and Build Chatbot Application for Tourism Object Information in Bengkulu City. TECHNOVATE: Journal of Information Technology and Strategic Innovation Management. 2024; 1(1): 28-34. doi: 10.52432/technovate.1.1.2024.28-34

[21] Chen J, Theeramunkong T, Supnithi T, et al. Knowledge and Systems Sciences. Springer Singapore; 2017. doi: 10.1007/978-981-10-6989-5

[22] Piau A, Crissey R, Brechemier D, et al. A smartphone Chatbot application to optimize monitoring of older patients with cancer. International Journal of Medical Informatics. 2019; 128: 18-23. doi: 10.1016/j.ijmedinf.2019.05.013

[23] Hassenzahl M, Diefenbach S, Göritz A. Needs, affect, and interactive products—Facets of user experience. Interacting with Computers. 2010; 22(5): 353-362. doi: 10.1016/j.intcom.2010.04.002

[24] Lamas D, Loizides F, Nacke L, et al. Human-Computer Interaction—INTERACT 2019. Springer International Publishing; 2019. doi: 10.1007/978-3-030-29390-1

[25] Berni A, Borgianni Y. Making Order in User Experience Research to Support Its Application in Design and Beyond. Applied Sciences. 2021; 11(15): 6981. doi: 10.3390/app11156981

[26] Yusof N, Hashim NL, Hussain A. A Conceptual User Experience Evaluation Model on Online Systems. International Journal of Advanced Computer Science and Applications. 2022; 13(1). doi: 10.14569/ijacsa.2022.0130153

[27] Redmiles EM. User Concerns & Tradeoffs in Technology-facilitated COVID-19 Response. Digital Government: Research and Practice. 2020; 2(1): 1-12. doi: 10.1145/3428093

[28] Williams G, Tushev M, Ebrahimi F, et al. Modeling user concerns in Sharing Economy: the case of food delivery apps. Automated Software Engineering. 2020; 27(3-4): 229-263. doi: 10.1007/s10515-020-00274-7

[29] Kim TS, Lee Y, Chang M, et al. Cells, Generators, and Lenses: Design Framework for Object-Oriented Interaction with Large Language Models. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology; 2023. doi: 10.1145/3586183.3606833

[30] Wu T, Terry M, Cai CJ. AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. In: Proceedings of the CHI Conference on Human Factors in Computing Systems; 2022. doi: 10.1145/3491102.3517582

[31] Glikson E, Woolley AW. Human Trust in Artificial Intelligence: Review of Empirical Research. Academy of Management Annals. 2020; 14(2): 627-660. doi: 10.5465/annals.2018.0057

[32] Gillath O, Ai T, Branicky MS, et al. Attachment and trust in artificial intelligence. Computers in Human Behavior. 2021; 115: 106607. doi: 10.1016/j.chb.2020.106607

[33] Ryan M. In AI We Trust: Ethics, Artificial Intelligence, and Reliability. Science and Engineering Ethics. 2020; 26(5): 2749-2767. doi: 10.1007/s11948-020-00228-y

[34] Omrani N, Rivieccio G, Fiore U, et al. To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change. 2022; 181: 121763. doi: 10.1016/j.techfore.2022.121763

[35] Bedué P, Fritzsche A. Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption. Journal of Enterprise Information Management. 2021; 35(2): 530-549. doi: 10.1108/jeim-06-2020-0233

[36] Vereschak O, Bailly G, Caramiaux B. How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies. Proceedings of the ACM on Human-Computer Interaction. 2021; 5(CSCW2): 1-39. doi: 10.1145/3476068

[37] Toreini E, Aitken M, Coopamootoo K, et al. The relationship between trust in AI and trustworthy machine learning technologies. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. doi: 10.1145/3351095.3372834

[38] Ferrario A, Loi M. How Explainability Contributes to Trust in AI. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. doi: 10.1145/3531146.3533202

[39] von Eschenbach WJ. Transparency and the Black Box Problem: Why We Do Not Trust AI. Philosophy & Technology. 2021; 34(4): 1607-1622. doi: 10.1007/s13347-021-00477-0

[40] Kaplan AD, Kessler TT, Brill JC, et al. Trust in Artificial Intelligence: Meta-Analytic Findings. Human Factors: The Journal of the Human Factors and Ergonomics Society. 2021; 65(2): 337-359. doi: 10.1177/00187208211013988

[41] Emaminejad N, Maria North A, Akhavian R. Trust in AI and Implications for AEC Research: A Literature Analysis. In: Proceedings of the Computing in Civil Engineering 2021. doi: 10.1061/9780784483893.037

[42] Luo B, Lau RYK, Li C, et al. A critical review of state‐of‐the‐art chatbot designs and applications. WIREs Data Mining and Knowledge Discovery. 2021; 12(1). doi: 10.1002/widm.1434

[43] Chaves AP, Gerosa MA. How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design. International Journal of Human—Computer Interaction. 2020; 37(8): 729-758. doi: 10.1080/10447318.2020.1841438

[44] Zhou L, Gao J, Li D, et al. The design and implementation of xiaoice, an empathetic social chatbot. Comput Linguist. 2020; 46(1): 53-93.

[45] Rahman AM, Mamun AA, Islam A. Programming challenges of chatbot: Current and future prospective. In: Proceedings of the 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). doi: 10.1109/r10-htc.2017.8288910

[46] Skjuve M, Følstad A, Fostervold KI, et al. My Chatbot Companion - a Study of Human-Chatbot Relationships. International Journal of Human-Computer Studies. 2021; 149: 102601. doi: 10.1016/j.ijhcs.2021.102601

[47] Følstad A, Araujo T, Law ELC, et al. Future directions for chatbot research: an interdisciplinary research agenda. Computing. 2021; 103(12): 2915-2942. doi: 10.1007/s00607-021-01016-7

[48] Thorat SA, Jadhav V. A Review on Implementation Issues of Rule-based Chatbot Systems. SSRN Electronic Journal. 2020. doi: 10.2139/ssrn.3567047

[49] Kumar R, Ali MM. A review on chatbot design and implementation techniques. Int J Eng Technol. 2020; 7(11): 2791-2800.

[50] Nagarhalli TP, Vaze V, Rana NK. A Review of Current Trends in the Development of Chatbot Systems. In: Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). doi: 10.1109/icaccs48705.2020.9074420

[51] Shingte K, Chaudhari A, Patil A, et al. Chatbot Development for Educational Institute. SSRN Electronic Journal. 2021. doi: 10.2139/ssrn.3861241

[52] Casas J, Tricot MO, Abou Khaled O, et al. Trends & Methods in Chatbot Evaluation. In: Proceedings of the 2020 International Conference on Multimodal Interaction. doi: 10.1145/3395035.3425319

[53] Santos GA, de Andrade GG, Silva GRS, et al. A Conversation-Driven Approach for Chatbot Management. IEEE Access. 2022; 10: 8474-8486. doi: 10.1109/access.2022.3143323

[54] Abdellatif A, Costa D, Badran K, et al. Challenges in Chatbot Development. In: Proceedings of the 17th International Conference on Mining Software Repositories; 2020. doi: 10.1145/3379597.3387472

[55] Ericsson KA, Simon HA. How to study thinking in everyday life: Contrasting think-aloud protocols with descriptions and explanations of thinking. Mind, Culture, and Activity. 1998; 5(3): 178-186.

Published

2024-08-17

How to Cite

Xu, Y., Gao, W., Wang, Y., Shan , X., & Lin, Y.-S. (2024). Enhancing user experience and trust in advanced LLM-based conversational agents. Computing and Artificial Intelligence, 2(2), 1467. https://doi.org/10.59400/cai.v2i2.1467

Download Citation

Issue

Vol. 2 No. 2 (2024)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Prof. Shaohua Wan
University of Electronic Science and Technology of China, China

eISSN

3029-2786

Publication Frequency

Semi-annual

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2024

Vol 2, No 2 (2024)

Vol 2, No 1 (2024)

2023

Vol 1, No 1 (2023)

Featured Articles

Identifying voices using convolution neural network models AlexNet and ResNet

Deep learning (DL) techniques which implement deep neural networks became popular due to the increase of high-performance computing facilities. DL achieves higher power and flexibility due to its ability to process many features when it deals with unstructured data. DL algorithm passes the data through several layers; each layer is capable of extracting features progressively and passes it to the next layer. Initial layers extract low-level features, and succeeding layers combine features to form a complete representation. This research attempts to utilize DL techniques for identifying sounds. The development in DL models has extensively covered classification and verification of objects through images. However, there have not been any notable findings concerning identification and verification of the voice of an individual from different other individuals using DL techniques. Hence, the proposed research aims to develop DL techniques capable of isolating the voice of an individual from a group of other sounds and classify them based on the use of convolutional neural networks models AlexNet and ResNet, that are used in voice identification. We achieved the classification accuracy of ResNet and AlexNet model for the problem of voice identification is 97.2039 % and 65.95% respectively, in which ResNet model achieves the best result.

Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare

The integration of artificial intelligence (AI) has brought about a paradigm shift in the landscape of Neurosurgery and Neurology, revolutionizing various facets of healthcare. This article meticulously explores seven pivotal dimensions where AI has made a substantial impact, reshaping the contours of patient care, diagnostics, and treatment modalities. AI’s exceptional precision in deciphering intricate medical imaging data expedites accurate diagnoses of neurological conditions. Harnessing patient-specific data and genetic information, AI facilitates the formulation of highly personalized treatment plans, promising more efficacious therapeutic interventions. The deployment of AI-powered robotic systems in neurosurgical procedures not only ensures surgical precision but also introduces remote capabilities, mitigating the potential for human error. Machine learning models, a core component of AI, play a crucial role in predicting disease progression, optimizing resource allocation, and elevating the overall quality of patient care. Wearable devices integrated with AI provide continuous monitoring of neurological parameters, empowering early intervention strategies for chronic conditions. AI’s prowess extends to drug discovery by scrutinizing extensive datasets, offering the prospect of groundbreaking therapies for neurological disorders. The realm of patient engagement witnesses a transformative impact through AI-driven chatbots and virtual assistants, fostering increased adherence to treatment plans. Looking ahead, the horizon of AI in Neurosurgery and Neurology holds promises of heightened personalization, augmented decision-making, early intervention, and the emergence of innovative treatment modalities. This narrative is one of optimism and collaboration, depicting a synergistic partnership between AI and healthcare professionals to propel the field forward and significantly enhance the lives of individuals grappling with neurological challenges. This article provides an encompassing view of AI’s transformative influence in Neurosurgery and Neurology, highlighting its potential to redefine the landscape of patient care and outcomes.

Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Application of computer vision in livestock and crop production—A review

Nowadays, it is a challenge for farmers to produce healthier food for the world population and save land resources. Recently, the integration of computer vision technology in field and crop production ushered in a new era of innovation and efficiency. Computer vision, a subfield of artificial intelligence, leverages image and video analysis to extract meaningful information from visual data. In agriculture, this technology is being utilized for tasks ranging from disease detection and yield prediction to animal health monitoring and quality control. By employing various imaging techniques, such as drones, satellites, and specialized cameras, computer vision systems are able to assess the health and growth of crops and livestock with unprecedented accuracy. The review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture, highlighting its role in optimizing farming practices and enhancing agricultural productivity.