-------- Forwarded Message --------
Subject: [AISWorld] CFP: Artificial Intelligence Implications for IS
Research Methods Minitrack
Date: Sat, 11 Jul 2020 16:14:03 +0000
From: Rohit Nishant <rohit.nishant(a)fsa.ulaval.ca>
To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org>
HICSS 54, Jan 5-8, 2021
Track: Organizational Systems and Technology
Minitrack: ARTIFICIAL INTELLIGENCE IMPLICATIONS FOR IS RESEARCH METHODS
This minitrack focuses on the integration of artificial intelligence
(AI) tools in more traditional research methods, as well as the role of
the IS field in developing new digital and automated research methods.
IS research has witnessed evolutions of different methodologies and
methodological paradigms at regular intervals. As upcoming progress in
AI is expected to fundamentally transform the very nature of work in all
fields, including academia, scholars will have to contemplate its
potential integration in their research practices. Some AI-based
features have already been implemented in academic research, notably for
data selection, sample allocation, and text analytics. There are also
machine learning based tools such as iris.ai available for literature
review. In that respect, it becomes important to start to reflect on the
potential and implications of AI with academic research, as well as our
own readiness for the forthcoming integration of AI into our work.
This minitrack covers issues related to the design, development, and
application of AI-based features for academic research. AI-based
features can include using Natural Language Processing (NLP) to review
research papers, using automated data modeling tool for empirical
analysis, and AI for identifying relationships inherent in a phenomenon.
This minitrack also seeks to explore research opportunities and
challenges associated with the automation of research, including
epistemological, methodological, and ethical implications. Submissions
may include research papers (theoretical and/or empirical), as well as
design studies, literature reviews, and research commentaries.
Topics of interest are related to at least five main themes associated
to the integration of AI in research methods, and relevant to the IS
community:
Integration of AI into traditional research methods and development of
new research methods, which will include
· Better access to Big Data
· Improvement of pattern recognition accuracy
· Development of new NLP algorithms for text analysis
· Improving predictive and prescriptive power of research models,
therefore shifting focus from inferential statistics to prediction
· New research directions and opportunities uncovered by AI capabilities
including
· Improved understanding of existing phenomenon
· Exploration of new topics and phenomenon
· Discovery of new relationships, hitherto unexplored previously
· Quality and evaluation of AI-supported research, including
· Integration of design principles to evaluate research tools
· Development of new metrics to assess AI-supported research
· Ethical considerations related to the use of AI in academic research
· Role of the IS field in accompanying AI-based changes in academia
Important Dates:
• Paper Submission Deadline: July 15, 2020, 11:59 p.m. HST
Minitrack Co-Chairs:
Mathieu Templier (Primary Contact)
Université Laval
mathieu.templier(a)fsa.ulaval.ca<mailto:mathieu.templier@fsa.ulaval.ca>
Rohit Nishant
Université Laval
rohit.nishant(a)fsa.ulaval.ca<mailto:rohit.nishant@fsa.ulaval.ca>
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