AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Khurana, A.; Chilana, P. K.

Understanding Novice Users' Mental Models of Gesture Discoverability and Designing Effective Onboarding Proceedings Article

In: UbiComp Companion - Companion ACM Int. Jt. Conf. Pervasive Ubiquitous Comput., pp. 290–295, Association for Computing Machinery, Inc, 2024, ISBN: 979-840071058-2 (ISBN).

Abstract | Links | BibTeX | Tags: Augmented Reality, Gesture discoverability, Gesture recognition, Help seeking, help-seeking, Immersive, Language Model, Large language model, large language models, Mental model, Mixed reality, Novice user, Onboarding, Prompt-based interaction, prompt-based interactions

@inproceedings{khurana_understanding_2024,

title = {Understanding Novice Users' Mental Models of Gesture Discoverability and Designing Effective Onboarding},

author = {A. Khurana and P. K. Chilana},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206148797&doi=10.1145%2f3675094.3678370&partnerID=40&md5=e16aa1b52d2ccbe8bdf882be4ff57fd7},

doi = {10.1145/3675094.3678370},

isbn = {979-840071058-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {UbiComp Companion - Companion ACM Int. Jt. Conf. Pervasive Ubiquitous Comput.},

pages = {290–295},

publisher = {Association for Computing Machinery, Inc},

abstract = {A variety of consumer Augmented Reality (AR) applications have been released on mobile devices and novel immersive headsets over the last five years, creating a breadth of new AR-enabled experiences. However, these applications, particularly those designed for immersive headsets, require users to employ unfamiliar gestural input and adopt novel interaction paradigms. This leap forward intensifies the complexity of help-seeking and onboarding needs for the end-users. Recent emergence of artificial intelligence (AI)powered in-context help tools has become potential alternatives to onboarding and search methods. However, non-technical users struggle with prompt-based interactions within LLMs that offer human-like language capabilities, which is unique, but can also be unreliable. My doctoral research aims to (1) understand how novice users discover gestural interactions and classify the types of interaction challenges they face; (2) investigate the nuances in users' mental models of emerging technologies, such as LLMs and AR; and, (3) explore the design of onboarding that enhances gesture discoverability and their application within the AR environments. © 2024 Copyright held by the owner/author(s).},

keywords = {Augmented Reality, Gesture discoverability, Gesture recognition, Help seeking, help-seeking, Immersive, Language Model, Large language model, large language models, Mental model, Mixed reality, Novice user, Onboarding, Prompt-based interaction, prompt-based interactions},

pubstate = {published},

tppubtype = {inproceedings}

}

Hong, J.; Lee, Y.; Kim, D. H.; Choi, D.; Yoon, Y. -J.; Lee, G. -C.; Lee, Z.; Kim, J.

A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models Proceedings Article

In: A., Vallgarda; L., Jonsson; J., Fritsch; S.F., Alaoui; C.A., Le Dantec (Ed.): Proc. ACM Des. Interact. Syst. Conf., pp. 1857–1874, Association for Computing Machinery, Inc, 2024, ISBN: 979-840070583-0 (ISBN).

Abstract | Links | BibTeX | Tags: 'current, Computational Linguistics, Context- awareness, Context-Aware, context-awareness, conversational agent, Conversational Agents, Divergents, Language Model, Large-language model, large-language models, Metaverse, Metaverses, Model-based OPC, Onboarding, User interfaces, Virtual Reality

@inproceedings{hong_context-aware_2024,

title = {A Context-Aware Onboarding Agent for Metaverse Powered by Large Language Models},

author = {J. Hong and Y. Lee and D. H. Kim and D. Choi and Y. -J. Yoon and G. -C. Lee and Z. Lee and J. Kim},

editor = {Vallgarda A. and Jonsson L. and Fritsch J. and Alaoui S.F. and Le Dantec C.A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200340104&doi=10.1145%2f3643834.3661579&partnerID=40&md5=5fe96b5155ca45c6d7a0d239b68f2b30},

doi = {10.1145/3643834.3661579},

isbn = {979-840070583-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. ACM Des. Interact. Syst. Conf.},

pages = {1857–1874},

publisher = {Association for Computing Machinery, Inc},

abstract = {One common asset of metaverse is that users can freely explore places and actions without linear procedures. Thus, it is hard yet important to understand the divergent challenges each user faces when onboarding metaverse. Our formative study (N = 16) shows that frst-time users ask questions about metaverse that concern 1) a short-term spatiotemporal context, regarding the user’s current location, recent conversation, and actions, and 2) a long-term exploration context regarding the user’s experience history. Based on the fndings, we present PICAN, a Large Language Model-based pipeline that generates context-aware answers to users when onboarding metaverse. An ablation study (N = 20) reveals that PICAN’s usage of context made responses more useful and immersive than those generated without contexts. Furthermore, a user study (N = 21) shows that the use of long-term exploration context promotes users’ learning about the locations and activities within the virtual environment. © 2024 Copyright held by the owner/author(s).},

keywords = {'current, Computational Linguistics, Context- awareness, Context-Aware, context-awareness, conversational agent, Conversational Agents, Divergents, Language Model, Large-language model, large-language models, Metaverse, Metaverses, Model-based OPC, Onboarding, User interfaces, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}