Skip to Main Content

Artificial Intelligence & Research

black, white, blue photo with a brain in the background and the words "Artificial Intelligence and Research Resource Guide"

AI & Research Guide

This guide contains links to resources about AI and machine learning as they pertain to the work of research.

Elon Showcase - Recent Scholarship in or around AI

  1. Akben, M., Mercado, B., & McSweeney, J. (2025). Bringing Historical Management Theories to Life: An Experiential Exercise Using Generative AI. Management Teaching Review.
  2. Arangala, C. (2023). Linear Algebra with Machine Learning and Data. Chapman and Hall/CRC.
  3. Braun, D., Han, Y., & Wang, H. E. (2023). The application of feed forward neural networks to merger arbitrage: A return-based analysis. Finance Research Letters, 58, 104391.
  4. Chen, C., & Sundar, S. S. (2023). Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–11.
  5. Chen, C., & Sundar, S. S. (2024). Communicating and combating algorithmic bias: effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust. Human–Computer Interaction, 1-37.
  6. Chen, C., Lee, S., Jang, E., & Sundar, S. S. (2024, September). Is Your Prompt Detailed Enough? Exploring the Effects of Prompt Coaching on Users' Perceptions, Engagement, and Trust in Text-to-Image Generative AI Tools. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (pp. 1-12).
  7. Chen, C., Liao, M., & Sundar, S. S. (2024, September). When to explain? Exploring the effects of explanation timing on user perceptions and trust in AI systems. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (pp. 1-17).
  8. Chen, C., Liao, M., Walther, J. B., & Sundar, S. S. (2024). When an ai doctor gets personal: The effects of social and medical individuation in encounters with human and ai doctors. Communication Research, 51(7), 747-781.
  9. Ferrell, O. C., Harrison, D. E., Ferrell, L. K., Ajjan, H., & Hochstein, B. W. (2024). A theoretical framework to guide AI ethical decision making. AMS Review, 1-15.
  10. Golding, J. M., Lippert, A., Neuschatz, J. S., Salomon, I., & Burke, K. (2024). Generative AI and College Students: Use and Perceptions. Teaching of Psychology, 00986283241280350.
  11. Jung, Y., Chen, C., Jang, E., & Sundar, S. S. (2024, May). Do We Trust ChatGPT as much as Google Search and Wikipedia?. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-9).
  12. Keefe, A. J., & Hament, B. (2024, May). Artificial Intelligence (AI) Voice Module for Robotic Service Dog. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 296-300). IEEE.
  13. Kpene, G. E., Lokpo, S. Y., & Darfour-Oduro, S. A. (2025). Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?. BMC Endocrine Disorders, 25(1), 9.
  14. Palliyaguru, N., Chennamangalam, J., Liyanage, S., Wellalage, B. K. H., Arangala, C., Armstrong, N. M., & Palliyaguru, D. L. (2024). Geographical mapping of colorectal cancer incidence risk factors in the United States using statistical and machine learning approaches.
  15. Petrescu, M., Ajjan, H., & Harrison, D. (2023a). The Role of AI Agents in Spreading and Detecting Fake Online Reviews: A Systematic Review: An Abstract. In B. Jochims & J. Allen (Eds.), Optimistic Marketing in Challenging Times: Serving Ever-Shifting Customer Needs (pp. 333–334). Springer Nature Switzerland.
  16. Petrescu, M., Ajjan, H., & Harrison, D. L. (2023b). Man vs machine – Detecting deception in online reviews. Journal of Business Research, 154, 113346.
  17. Ryu, H., Miller, J., Teymuroglu, Z., Wang, X., Booth, V., & Campbell, S. A. (2021). Spatially localized cluster solutions in inhibitory neural networks. Mathematical Biosciences, 336, 108591.
  18. Safarnejad, L., Xu, Q., Ge, Y., & Chen, S. (2021). A Multiple Feature Category Data Mining and Machine Learning Approach to Characterize and Detect Health Misinformation on Social Media. IEEE Internet Computing, 25(5), 43–51.
  19. Vagt, B., Foster, M., & Blackmon, R. (2023, April). Designing and Building a Deep Imaging Multi-Parametric Optical Coherence Tomography System for Disease Assessment. In 2023 Systems and Information Engineering Design Symposium (SIEDS) (pp. 292-296). IEEE.
  20. Xia, L. (2022). Historical profile will tell? A deep learning-based multi-level embedding framework for adverse drug event detection and extraction. Decision Support Systems, 160, 113832.
  21. Xia, L., Shen, W., Fan, W., & Wang, G. A. (2024). Knowledge-Aware Learning Framework Based on Schema Theory to Complement Large Learning Models. Journal of management information systems, 41(2), 453-486.
  22. Xie, T., Ge, Y., Xu, Q., & Chen, S. (2023). Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance. AI, 4(1), 333–347.