Artificial Intelligence in University Teaching: A Systematic Review of Impacts, Challenges and Trends (2020–2025).
DOI:
https://doi.org/10.70577/rp9hf309Keywords:
Artificial Intelligence, Higher Education, University Teaching, Systematic Review, Educational Innovation, Learning Analytics, Academic Integrity, Digital TransformationAbstract
Artificial Intelligence (AI) has emerged as a strategic tool in higher education, particularly as pedagogical support in university teaching. However, its integration into teaching–learning processes reveals a critical issue: the lack of structured pedagogical frameworks and robust empirical evidence to support its effective implementation. This gap generates tensions between technological innovation, traditional teaching practices, and institutional demands related to ethics, assessment, and educational quality. Therefore, this study aimed to systematically analyze recent scientific production on the integration of AI in higher education.
A systematic literature review was conducted following the PRISMA protocol, with a search performed in the Scopus database, considering articles published between 2020 and 2025. After applying predefined inclusion and exclusion criteria, eighteen studies were selected for qualitative analysis. The findings were organized into five thematic areas: the use of generative AI by students, AI-mediated pedagogical innovation, intelligent assessment and learning analytics, ethics and academic integrity, and disciplinary digital transformation.
The evidence indicates that AI enhances personalized learning, improves formative feedback, and optimizes teaching processes. Nevertheless, significant challenges persist, particularly regarding institutional regulation, faculty training in digital competencies, and the redefinition of assessment systems. It is concluded that AI represents a significant opportunity for pedagogical innovation in higher education; however, its effective integration requires solid pedagogical frameworks and more rigorous research designs to consolidate empirical evidence of its medium- and long-term impact.
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