Vision, challenges, roles and research issues of Artificial Intelligence in Education

By Gwo-Jen Hwang, Haoran Xie, Benjamin Wan-Sang Wah, Dragan Gasevic. Computers and Education: Artificial Intelligence, 2020

1,159 citations recorded by the source corpus.

Abstract The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/26f7201ae06558b8c2e3a2f2ffa5aef7a1b75a0e

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Citations in the corpus

Impact of AI assistance on student agency (2023, background): “An existing limitation with many AI-Edtech is that they have been shown to have a weak connection to theoretical pedagogical perspectives Chen, Xie, Zou, and Hwang (2020); Zawacki-Richter, Marín, Bond, and Gouverneur (2019).”
Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy (2023, methodology): “There are several approaches to using AI in higher education, including using AI to personalize learning, facilitate communication, and enhance assessment (Chen et al., 2020; Hwang et al., 2020).”
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “Accuracy of AI prediction models: From the AI model perspective Online higher education has attracted extensive attention in the COVID-19 period with a goal to improving the quality of personalization, monitoring and evaluation in learning (Hwang et al., 2020).”
Ethical principles for artificial intelligence in education (2022, methodology): “…that are deemed unfeasible in the conventional classroom settings, for instance, the implementation of artificial neural networks, machine learning or CALL (Computer-Assisted Lan - guage Learning) in formal, non-formal and informal learning scenarios (Holmes et al., 2019; Hwang et al., 2020 ).”
Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning (2023, background): “In the education contexts, AI technologies hold immense potential to transform traditional instructional methods, providing personalized learning experiences tailored to individual needs and preferences (Hwang et al., 2020).”
Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “Some studies argued that AI technology can effectively promote students’ personalized learning (Ahmad et al., 2022), advance their knowledge acquisition and motivate students’ learning using intelligent agents (Chen et al., 2020a, 2020b; Hwang et al., 2020).”
Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study (2024, background): “Concomitantly, AI in education (AIED) has emerged as a vibrant academic field, extending the capability of AI not just to learners but also to educators and institutions alike (Chen et al., 2020; Hwang et al., 2020; Nemorin et al., 2023).”
Learning and development (2013, positive): “It is probably worth agreeing with researchers who consider AI capabilities insufficiently integrated with modern educational platforms: usually, teachers use artificial intelligence autonomously, without integration with other educational technologies (Hwang et al., 2020; Ouyang & Jiao, 2021).”
Leading teachers' perspective on teacher-AI collaboration in education (2023, positive): “…support individual students to master subject-matter knowledge and skills based on their learning status, preferences, or personal characteristics (Hwang et al., 2020), recent studies move forward to apply AIED to augment students’ higher-order thinking such as creativity, computational thinking…”
Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT (2023, background): “The use of chatbots to enhance students’ language learning experience has gained much attention in recent years (Huang et al., 2023; Hwang et al., 2020; Jeon, 2021; Jeon, 2022a; Lee & Jeon, 2022).”