Challenges and Future Directions of Big Data and Artificial Intelligence in Education
By Hui Luan, P. Géczy, Hollis Lai, Janice D. Gobert, Stephen J. H. Yang, H. Ogata, J. Baltes, R. Guerra, Ping Li, Chin-Chung Tsai. Frontiers in Psychology, 2020
477 citations recorded by the source corpus.
We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.
Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/9198d6ab8ad1c6f1527a7d4adf06809848bf23c7
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Citations in the corpus
- Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning (2023, background): “…collectively emphasize the transformative nature of AI-mediated language instruction (Kim et al., 2019), supporting the existing literature regarding the potential advantages of AI in education in general (e.g., Malik et al., 2019; Chen et al., 2020; Luan et al., 2020; Ouyang and Jiao, 2021).”
- A comprehensive AI policy education framework for university teaching and learning (2023, background): “Based on these concerns, AI policies in education fix their eyes on addressing a number of issues: literacy education to prevent inequalities in the use of digital technologies (Southgate, 2020; UNESCO, 2021b); essential values of traditional forms of teaching and learning such as teacher-student an”
- Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “Teachers need to upgrade their skills and knowledge, and connect the tools to content knowledge and pedagogy through continuous professional training (Kim et al., 2021) such as technical support, guidelines, and teacher education programs (Chiu & Chai, 2020; Luan et al., 2020).”
- Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “…focus on nat-ural dialogue interaction should be placed when designing an intelligent personal assistant (Murad & Munteanu, 2019), whereas if the system addresses an educational context, domain-specific concerns should be catered for in order to make it usable (Langley, 2019; Luan et al., 2020).”
- The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence (2022, background): “In recent years, rapid improvements in artificial intelligence (AI) technologies have affected all social systems, including the economy, politics, science, and education (Luan et al., 2020, Stephanidis et al., 2019).”