About the scope and nature of AI in Higher Education: A meta-systematic review 

Artificial Intelligence has been part of education research since the 1960s, but recent advancements have brought it into classrooms. Tools like ChatGPT and DALL-E have sparked discussions about their impact and the need for regulation. Governments worldwide are stepping in to oversee AI use in education. To provide a solid understanding of AI in Higher Education, Bond et al. (2024) offer a tertiary review approach, looking at existing secondary studies to map the state of AI in higher education (AIHEd) and reduce research duplication by analyzing the types of evidence syntheses, publication sources, collaboration patterns, and technologies used. They suggest a new framework for a shared language among researchers and explore the leading applications, key findings, benefits, challenges, and research gaps in AIHEd secondary research, which will be presented in this article. 


The analysis encompasses 66 reviews focusing on AI applications or AI in higher education. These reviews vary in their coverage of the field’s benefits, challenges, and research gaps. Some reviews specifically address AI in higher education (AIHEd) affordances, while others focus on broader AI applications. Among many research questions that relate to the scope of research about AIHed, some questions address practical implications and results:   

Benefits of using AI in Higher Education: 

  • Personalized learning is a significant benefit, enabling tailored educational materials to meet individual students’ needs. 
  • AI provides greater insight into student understanding and positively influences learning outcomes, albeit with limited evidence. 
  • It reduces planning and administration time for teachers through the automation of routine tasks.
  • AI enhances equity in education by providing more accessible and engaging learning opportunities. 
  • It facilitates precise assessment and feedback, improving grading accuracy and timely feedback provision. 

Challenges of using AI in Higher Education: 

  • Ethical considerations like privacy protection and balancing human and machine-assisted learning pose challenges. 
  • Curriculum development and infrastructure issues hinder AI integration into educational systems. 
  • Many educators need more technical knowledge to effectively use AI tools.
  • The perception of a shift in authority from humans to AI systems raises concerns among stakeholders.

Which research gaps did the authors identify?

  • Ethical implications demand further exploration, including privacy protection and the social impact of AI.
  • Methodological approaches need enhancement, focusing on rigorous research designs and evaluation methodologies.
  • The study contexts should be broadened to include diverse populations, stakeholders, and educational contexts, especially in developing countries and non-STEM disciplines.
  • There is a need for more collaboration in the four key areas: development of AI applications, designing and teaching AI curricula, researching AIHEd, and conducting evidence syntheses. Users should be included in the development, as well as stakeholders from various areas like data or computer science, researchers from human or social sciences, and scientists from all over the world.
References
  • Bond, M., Khosravi, H., De Laat, M. et al. A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. Int J Educ Technol High Educ 21, 4 (2024). https://doi.org/10.1186/s41239-023-00436-z

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