[AISWorld] Call for Papers - HICSS 2025, Mini Track: TECHNOLOGICAL ADVANCEMENTS IN DIGITAL COLLABORATION WITH GENERATIVE AI AND LARGE LANGUAGE MODELS

[Tao, Jie]

Dear colleagues,

We would love to invite you to submit your related research to our mini track at HICSS 2025. Please find the call for paper below.


Technological advancements are reshaping the landscape of digital collaboration. It is also important for digital collaboration to harness emerging technologies. In particular, generative AI and large language models, which have witnessed tremendous growth in recent years, empower next-generation digital collaboration and transform the dynamics and ways of it. For instance, large language models are widely adopted by businesses to assist users in a diverse range of open-ended tasks, offering novel ways to enhance the productivity and creativity in digital collaboration. However, generative AI and large language models are also facing their own set of challenges such as hallucination. Therefore, there is a strong need to both effectively leverage and continuously advance these technologies to enhance digital collaboration.

This minitrack explores advanced technologies in enterprise collaboration, social networking, and human-computer interaction, with a specific focus on prompt engineering, Parameter Efficient Fine Tuning (PEFT), RAG, and other relevant techniques. These advancements bring benefits to efficiency, productivity, creativity, emotional support, and even human well-being and organizational competitiveness. This showcases the seamless integration of human collaboration with generative AI, emphasizing transparency and interpretability across digital environments. We welcome technical discussions of the advancement, development, and adaptation of such techniques to enhance the quality, productivity, creativity, user satisfaction, and cost efficiency in terms of digital collaboration. Topics of interest include, but are not limited to:

  *   Generative AI Interaction with Prompt Engineering
  *   Zero-shot and Few-shots Learning and Prompt Engineering
  *   PEFT Applications for Digital Collaboration
  *   Effective instruction fine tuning
  *   Large Language Models (LLM) on social media contents
  *   Generative AI and Natural Language Processing (NLP)
  *   RAG and Prompt Engineering
  *   Human Feedback for Model Improvement
  *   Social and human factors in Prompt Engineering and RLHF
  *   LoRA of Generative AI and LLM
  *   Ethical Considerations in human-AI collaboration
  *   Privacy and security considerations in Parameter-Efficient Fine Tuning
  *   Collaborative frameworks with consideration of LLM
  *   Lifecycle and strategies for generative AI and LLM projects

Should you have any questions, please feel free to contact me or my co-chairs.


Jie Tao (Primary Contact)
Fairfield University
jtao at fairfield.edu

Lina Zhou
University of North Carolina at Charlotte
lzhou8 at uncc.edu

Xing Fang
Illinois State University
xfang13 at ilstu.edu