Abstract:

This study explores the integration and challenges of generative artificial intelligence (GenAI) in higher education, focusing on how educational policies can adapt. It examines the main challenges of implementing GenAI and strategies to maximize its benefits, emphasizing the need for scalable curricula, teacher training, ethical use, policy coherence, and technology accessibility.

Since 2020, the rise of GenAI, especially ChatGPT, has significantly impacted technology, hinting at transformative potential in education. This evolution requires balancing innovation with risk management. GenAI's influence has sparked debate, highlighting the need for comprehensive understanding.

GenAI advancements have accelerated due to interest, enhanced computing capabilities, and neural networks. Applications in research and productivity, backed by investments from companies like Amazon and OpenAI, position GenAI as transformative in education.

Digital technology is shaping education, promoting inclusivity and diversity. GenAI can enhance learning but requires curricular restructuring, resource investment, and ethical content generation. Success depends on educators' and students' skills in integrating GenAI.

Using literature mapping, the study identifies trends and opportunities in GenAI's application in higher education. Analysis shows a focus on keywords like "learning," "AI," and "education," indicating a trend towards integrating AI. This offers personalized teaching and enhanced learning experiences but requires teacher preparedness.

Integrating AI competencies into curricula is crucial for job readiness and adaptability. GenAI's dual use for teaching and research necessitates ongoing professional development for ethical and effective use.

Policy approaches highlight the need for adaptive, coherent, and evaluable policies, ensuring educational equity and accessibility while keeping pace with technology.

The study identifies three main challenges: scalable, updated AI curricula; comprehensive teacher training for ethical GenAI use; and adaptive, coherent policies. Strategic investments and academia-industry collaboration are essential.

In conclusion, integrating GenAI in higher education presents significant opportunities and challenges. Institutions and policymakers must strategically maximize benefits while addressing ethical and practical issues. Future research should explore socioeconomic factors and access to GenAI education, incorporating diverse industry and empirical perspectives.

This study offers valuable recommendations for maximizing GenAI's benefits and addressing challenges, emphasizing continuous curriculum updates, teacher training, and adaptive policies.

Keywords:

GenAI, higher education, curriculum, teacher training, policy adaptation.