mlsanityData to 7 Oct 2026

Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research

arXiv 2503.05770 ↗First posted 25 Feb 2025cs.CY cs.AI cs.LGcited by 4

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Abstract (arXiv)

The continuing, explosive developments in generative artificial intelligence (GenAI), built on large language models and related algorithms, has led to much excitement and speculation about the potential impact of this new technology. Claims include AI being poised to revolutionize business and society and dramatically change personal life. However, it remains unclear exactly how this technology, with its significantly distinct features from past AI technologies, has transformative potential. Nor is it clear how researchers in information systems (IS) should respond. In this paper, we consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts. Many existing papers on GenAI are either too technical for most IS researchers or lack the depth needed to appreciate the potential impacts of GenAI. We, therefore, attempt to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective. Specifically, we explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism, and the deep systemic and inherent properties of human-AI ecosystems. We retrace the evolution of AI that proceeded the level of adoption, adaption, and use found today, in order to propose future research on various impacts of GenAI in both business and society within the context of information systems research. Our efforts are intended to contribute to the creation of a well-structured research agenda in the IS community to support innovative strategies and operations enabled by this new wave of AI.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 135 references that are in the map. First posted on arXiv 25 Feb 2025.

Builds on

19 of 135 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  4. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  5. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  6. 2023Faithful Chain-of-Thought Reasoningcited by 285
  7. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  8. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  9. 2024xLAM: A Family of Large Action Models to Empower AI Agent Systemscited by 108
  10. 2023Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimismcited by 69
  11. 2020Intrinsic Reward Driven Imitation Learning via Generative Modelcited by 35
  12. 2023More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineeringcited by 13
7 more
  1. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Reportcited by 13
  2. 2023Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMscited by 9
  3. 2019Comprehensive Process Drift Detection with Visual Analyticscited by 8
  4. 2023Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMscited by 7
  5. 2024A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finishcited by 6
  6. 2022Methodological reflections for AI alignment research using human feedbackcited by 5
  7. 2016Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spannerscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.