IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/120135.html
   My bibliography  Save this paper

Student Reactions to AI-Replicant Professor in an Econ101 Teaching Video

Author

Listed:
  • Rosa-García, Alfonso

Abstract

This study explores student responses to AI-generated educational content, specifically a teaching video delivered by an AI-replicant of their professor. Utilizing ChatGPT-4 for scripting and Heygen technology for avatar creation, the research investigates whether students' awareness of the AI's involvement influences their perception of the content's utility. With 97 participants from first-year economics and business programs, the findings reveal a significant difference in valuation between students informed of the AI origin and those who were not, with the former group valuing the content less. This indicates a bias against AI-generated materials based on their origin. The paper discusses the implications of these findings for the adoption of AI in educational settings, highlighting the necessity of addressing student biases and ethical considerations in the deployment of AI-generated educational materials. This research contributes to the ongoing debate on the integration of AI tools in education and their potential to enhance learning experiences.

Suggested Citation

  • Rosa-García, Alfonso, 2024. "Student Reactions to AI-Replicant Professor in an Econ101 Teaching Video," MPRA Paper 120135, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:120135
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/120135/1/MPRA_paper_120135.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    2. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2023. "Generative AI at Work," Papers 2304.11771, arXiv.org, revised Nov 2024.
    3. Gary Charness & Brian Jabarian & John A. List, 2023. "Generation Next: Experimentation with AI," NBER Working Papers 31679, National Bureau of Economic Research, Inc.
    4. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    5. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    6. Jean-François Bonnefon & Fatimah Ishowo-Oloko & Zakariyah Soroye & Jacob W. Crandall & Iyad Rahwan & Tahal Rahwan, 2019. "Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation," Post-Print hal-04121730, HAL.
    7. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samuel Chang & Andrew Kennedy & Aaron Leonard & John A. List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," NBER Working Papers 33025, National Bureau of Economic Research, Inc.
    2. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    3. Cova, Joshua & Schmitz, Luuk, 2024. "A primer for the use of classifier and generative large language models in social science research," OSF Preprints r3qng, Center for Open Science.
    4. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Aug 2024.
    5. Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
    6. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    7. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
    8. Ali Goli & Amandeep Singh, 2023. "Exploring the Influence of Language on Time-Reward Perceptions in Large Language Models: A Study Using GPT-3.5," Papers 2305.02531, arXiv.org, revised Jun 2023.
    9. Evangelos Katsamakas, 2024. "Business models for the simulation hypothesis," Papers 2404.08991, arXiv.org.
    10. Yuan Gao & Dokyun Lee & Gordon Burtch & Sina Fazelpour, 2024. "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina," Papers 2410.19599, arXiv.org, revised Jan 2025.
    11. Christoph Engel & Max R. P. Grossmann & Axel Ockenfels, 2023. "Integrating machine behavior into human subject experiments: A user-friendly toolkit and illustrations," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2024_01, Max Planck Institute for Research on Collective Goods.
    12. Yiting Chen & Tracy Xiao Liu & You Shan & Songfa Zhong, 2023. "The emergence of economic rationality of GPT," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(51), pages 2316205120-, December.
    13. Jiafu An & Difang Huang & Chen Lin & Mingzhu Tai, 2024. "Measuring Gender and Racial Biases in Large Language Models," Papers 2403.15281, arXiv.org.
    14. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    15. Fabio Motoki & Valdemar Pinho Neto & Victor Rodrigues, 2024. "More human than human: measuring ChatGPT political bias," Public Choice, Springer, vol. 198(1), pages 3-23, January.
    16. Siting Estee Lu, 2024. "Strategic Interactions between Large Language Models-based Agents in Beauty Contests," Papers 2404.08492, arXiv.org, revised Oct 2024.
    17. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    18. Ayato Kitadai & Sinndy Dayana Rico Lugo & Yudai Tsurusaki & Yusuke Fukasawa & Nariaki Nishino, 2024. "Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?," Papers 2406.11426, arXiv.org.
    19. Navid Ghaffarzadegan & Aritra Majumdar & Ross Williams & Niyousha Hosseinichimeh, 2024. "Generative agent‐based modeling: an introduction and tutorial," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    20. Bauer, Kevin & Liebich, Lena & Hinz, Oliver & Kosfeld, Michael, 2023. "Decoding GPT's hidden "rationality" of cooperation," SAFE Working Paper Series 401, Leibniz Institute for Financial Research SAFE.

    More about this item

    Keywords

    AI-Generated Content; Virtual Avatars; Student Perceptions; Technology Adoption;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:120135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.