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Machine Learning

Generative Artificial Intelligence in Education: From Deceptive to Disruptive.

Marc Alier(Universitat Politècnica de Catalunya), Francisco José García‐Peñalvo(Universidad de Salamanca), Jorge D. Camba(Purdue University West Lafayette)
March 1, 2024International Journal of Interactive Multimedia and Artificial Intelligence148 citations
PDFDOI

148

Citations

10

Influential Citations

International Journal of Interactive Multimedia and Artificial Intelligence

Venue

2024

Year

Abstract

Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.

Analysis

Why This Paper Matters

Generative AI (GenAI) is rapidly transforming educational landscapes, offering tools that can create original content for teaching, assessment, and personalized learning. This paper, published in the International Journal of Interactive Multimedia and Artificial Intelligence, provides a timely overview of both the promise and perils of GenAI in education. As institutions grapple with student use of models like ChatGPT, this work serves as a critical resource for understanding how to harness GenAI's capabilities without compromising academic integrity. The paper's relevance is underscored by its 148 citations, indicating strong interest from the AI and education communities.

Technical Contributions

The paper's primary contribution is a conceptual framework for integrating GenAI into education, focusing on:

  • Personalized Learning: Using GenAI to generate custom content that adapts to individual student needs, enhancing engagement and understanding.
  • Assessment Automation: Employing GenAI to create quizzes, essay prompts, and even grade assignments, thereby reducing teacher workload and providing faster feedback.
  • Ethical Guidelines: Proposing best practices to ensure GenAI supports rather than replaces student learning, emphasizing transparency and originality.
  • Academic Integrity: Addressing concerns about authenticity and authorship when students use GenAI for assignments, and suggesting methods to maintain standards.

Results

As a review paper, no experimental results or quantitative metrics are provided. The paper synthesizes existing knowledge to argue that GenAI can augment education if deployed ethically. It does not compare different GenAI models or present performance benchmarks, focusing instead on qualitative insights and policy recommendations.

Significance

This paper is significant for its role in shaping the discourse on GenAI in education. By highlighting both opportunities (e.g., personalized learning, efficient assessment) and challenges (e.g., plagiarism, loss of critical thinking), it provides a balanced perspective that can guide educators, administrators, and policymakers. The work underscores the need for proactive ethical frameworks as GenAI becomes more pervasive, influencing future research on AI literacy and responsible AI use in academic settings.

generative-aieducationethicsassessmentpersonalized-learningacademic-integritymachine-learning
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