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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots (2306.03823v1)

Published 25 May 2023 in cs.CY, cs.AI, and cs.CL

Abstract: ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

Transformative Effects of ChatGPT on Modern Education: Implications and Challenges

The academic paper, "Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots," presents a comprehensive examination of the potential disruptions and opportunities associated with the integration of ChatGPT, an AI chatbot, into educational settings. Authored by Sukhpal Singh Gill and colleagues, this research piece explores the capacities and limitations of ChatGPT, evaluates its impact on learning environments, and addresses the multifaceted challenges it poses to traditional educational paradigms.

The paper systematically evaluates ChatGPT’s utility across various educational domains. In its preliminary evaluations, ChatGPT shows disparate performance across disciplines like finance, coding, and mathematics. These assessments highlight both potential benefits, such as automated content generation and personalized learning support, and significant drawbacks, including the generation of incorrect data and inability to detect plagiarism. Notably, the propensity for "hallucinations," or the inclusion of fictitious information within generated responses, poses critical challenges in contexts where accuracy and originality are unequivocal necessities.

Key Findings

  1. Functional Capabilities and Limitations: Utilizing AI and NLP, ChatGPT serves as an educational tool, offering coherent and informative responses to user-generated queries. Despite the wide applicability, the risk of misinformation and potential for leveraging AI to avoid original work merits close examination.
  2. Ethics and Equity: The paper stresses the need for evolved educational regulations to reflect these developments, emphasizing equity in access, particularly in resource-limited settings. Disparities in access to digital resources can exacerbate educational inequalities, necessitating institutional interventions.
  3. Impact on Assessment Practices: There is significant discourse on the threat ChatGPT poses to conventional assessment systems. As the capability of AI tools expands, educational evaluation methods must focus more on independent critical thinking, problem-solving, and the nuanced understanding that AI-generated solutions lack.
  4. Teaching and Learning Adaptations: Educators are encouraged to integrate AI responsibly into pedagogy, using ChatGPT as an aid rather than a replacement for critical cognitive activities. Teacher training is advocated to develop skills for detecting AI-assisted work and adjusting instructional materials appropriately.
  5. Future Considerations: The researchers call for regulatory measures to govern the use of ChatGPT in educational contexts effectively. This involves refining prompt strategies for more accurate outputs and using stochastic models, such as MDP, to enhance model design.

Implications and Prospective Developments

The advent of AI tools like ChatGPT is reshaping modern educational landscapes. However, its journey from the "trough of disillusionment" to the "plateau of productivity," as outlined by the Gartner Hype Cycle, reflects a trajectory that involves overcoming initial skepticism. As these models become more precise and integrated with productivity tools, a broader acceptance is anticipated, provided that educational entities balance AI's benefits with its challenges.

A pivotal role awaits regulatory bodies and educational administrators in establishing safeguards against AI misuse while also leveraging its potential to facilitate innovative learning strategies. The underlying message of the research advocates an adapted educational framework where the emphasis is placed on fostering skills that AI cannot replicate: creativity, ethical judgment, and human-centered problem-solving.

In conclusion, as the influence of AI, epitomized by ChatGPT, extends across educational systems worldwide, its integration should be approached with strategic foresight, emphasizing rigorous standards for academic integrity and an enhanced focus on developing durable cognitive skills. Future explorations of AI in education should prioritize transparency, student equity, and adaptive learning models capable of navigating and utilizing the evolving capabilities of AI technologies.

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Authors (21)
  1. Sukhpal Singh Gill (39 papers)
  2. Minxian Xu (36 papers)
  3. Panos Patros (6 papers)
  4. Huaming Wu (20 papers)
  5. Rupinder Kaur (6 papers)
  6. Kamalpreet Kaur (3 papers)
  7. Stephanie Fuller (1 paper)
  8. Manmeet Singh (30 papers)
  9. Priyansh Arora (3 papers)
  10. Ajith Kumar Parlikad (6 papers)
  11. Vlado Stankovski (5 papers)
  12. Ajith Abraham (30 papers)
  13. Soumya K. Ghosh (133 papers)
  14. Hanan Lutfiyya (12 papers)
  15. Salil S. Kanhere (96 papers)
  16. Rami Bahsoon (34 papers)
  17. Omer Rana (41 papers)
  18. Schahram Dustdar (72 papers)
  19. Rizos Sakellariou (13 papers)
  20. Steve Uhlig (25 papers)
Citations (188)