AHCI RESEARCH GROUP

Publications

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

OUR RESEARCH

Scientific Publications

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2024

Arrigo, M.; Farella, M.; Fulantelli, G.; Schicchi, D.; Taibi, D.

A Task-Interaction Framework to Monitor Mobile Learning Activities Based on Artificial Intelligence and Augmented Reality Proceedings Article

In: L.T., De Paolis; P., Arpaia; M., Sacco (Ed.): Lect. Notes Comput. Sci., pp. 325–333, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303171706-2 (ISBN).

Abstract | Links | BibTeX | Tags: Activity-based, Adversarial machine learning, Analytic technique, Augmented Reality, Contrastive Learning, Federated learning, Generative AI, Interaction framework, Learning Activity, Learning analytic framework, Learning Analytics Framework, Learning experiences, Learning patterns, Mobile Learning, Teachers'

@inproceedings{arrigo_task-interaction_2024,

title = {A Task-Interaction Framework to Monitor Mobile Learning Activities Based on Artificial Intelligence and Augmented Reality},

author = {M. Arrigo and M. Farella and G. Fulantelli and D. Schicchi and D. Taibi},

editor = {De Paolis L.T. and Arpaia P. and Sacco M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204618733&doi=10.1007%2f978-3-031-71707-9_26&partnerID=40&md5=8969f18ab0f10dcddf37e54265d10518},

doi = {10.1007/978-3-031-71707-9_26},

isbn = {03029743 (ISSN); 978-303171706-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15027 LNCS},

pages = {325–333},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the context in which the activities occur, the content they have access to, with peers and their teachers. The wider adoption of generative artificial intelligence introduces new interactions that researchers have to look at when learning analytics techniques are applied to monitor learning patterns. The task interaction framework proposed in this paper explores how AI-based tools affect student-content and student-context interactions during mobile learning activities, thus focusing on the interplay of Learning Analytics and Artificial Intelligence advances in the educational domain. A use case scenario that explores the framework’s application in a real educational context is also presented. Finally, we describe the architectural design of an environment that leverages the task interaction framework to analyze enhanced mobile learning experiences in which structured content extracted from a Knowledge Graph is elaborated by a large language model to provide students with personalized content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Activity-based, Adversarial machine learning, Analytic technique, Augmented Reality, Contrastive Learning, Federated learning, Generative AI, Interaction framework, Learning Activity, Learning analytic framework, Learning Analytics Framework, Learning experiences, Learning patterns, Mobile Learning, Teachers'},

pubstate = {published},

tppubtype = {inproceedings}

}

Xiao, Z.; Wang, T.; Wang, J.; Cao, J.; Zhang, W.; Dai, B.; Lin, D.; Pang, J.

UNIFIED HUMAN-SCENE INTERACTION VIA PROMPTED CHAIN-OF-CONTACTS Proceedings Article

In: Int. Conf. Learn. Represent., ICLR, International Conference on Learning Representations, ICLR, 2024.

Abstract | Links | BibTeX | Tags: Contact regions, Human joints, Interaction controls, Interaction framework, Quality control, Scene interactions, Strong correlation, Task executions, Task plan, Unified control, User friendly interface, Virtual Reality

@inproceedings{xiao_unified_2024,

title = {UNIFIED HUMAN-SCENE INTERACTION VIA PROMPTED CHAIN-OF-CONTACTS},

author = {Z. Xiao and T. Wang and J. Wang and J. Cao and W. Zhang and B. Dai and D. Lin and J. Pang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189112121&partnerID=40&md5=ed6c80431e6c18f32cdb9dd013fd60d0},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. Learn. Represent., ICLR},

publisher = {International Conference on Learning Representations, ICLR},

abstract = {Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and user-friendly interfaces, require further exploration for the practical application of HSI. This paper presents a unified HSI framework, named UniHSI, that supports unified control of diverse interactions through language commands. The framework defines interaction as “Chain of Contacts (CoC)”, representing steps involving human joint-object part pairs. This concept is inspired by the strong correlation between interaction types and corresponding contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To support training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.},

keywords = {Contact regions, Human joints, Interaction controls, Interaction framework, Quality control, Scene interactions, Strong correlation, Task executions, Task plan, Unified control, User friendly interface, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}