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

arXiv: https://arxiv.org/abs/2503.05770 · HTML: https://mlsanity.com/paper/2503.05770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 135 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Faithful Chain-of-Thought Reasoning](https://mlsanity.com/paper/2301.13379/) (2023)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [xLAM: A Family of Large Action Models to Empower AI Agent Systems](https://mlsanity.com/paper/2409.03215/) (2024)
- [Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism](https://mlsanity.com/paper/2305.18438/) (2023)
- [Intrinsic Reward Driven Imitation Learning via Generative Model](https://mlsanity.com/paper/2006.15061/) (2020)
- [More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering](https://mlsanity.com/paper/2311.09782/) (2023)
- [Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report](https://mlsanity.com/paper/2402.01733/) (2024)
- [Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs](https://mlsanity.com/paper/2309.05918/) (2023)
- [Comprehensive Process Drift Detection with Visual Analytics](https://mlsanity.com/paper/1907.06386/) (2019)
- [Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs](https://mlsanity.com/paper/2303.12810/) (2023)
- [A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish](https://mlsanity.com/paper/2403.16139/) (2024)
- [Methodological reflections for AI alignment research using human feedback](https://mlsanity.com/paper/2301.06859/) (2022)
- [Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners](https://mlsanity.com/paper/1611.00721/) (2016)

## Built on by (most cited first, 4 of 4)

- [LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models](https://mlsanity.com/paper/2506.06874/) (2025)
- [Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study](https://mlsanity.com/paper/2508.03710/) (2025)
- [Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand](https://mlsanity.com/paper/2604.06198/) (2026)
- [Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models](https://mlsanity.com/paper/2606.31404/) (2026)

## 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.