Application and theory gaps during the rise of Artificial Intelligence in Education
By Xieling Chen, Haoran Xie, Di Zou, Gwo-Jen Hwang. Computers and Education: Artificial Intelligence, 2020
861 citations recorded by the source corpus.
Abstract Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners’ answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners’ during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.
Subfields: Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/5434e470c21cde0af431202846c8c6d5d453b4ca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
Citations in the corpus
- Learning and development (2013, neutral): “The study by Chen et al. (2020b) describes the necessary theoretical foundations for using artificial intelligence in education.”
- The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education? (2023, neutral): “Chen, Xie, Zou, & Hwang, (2020) highlighted natural language processing and machine learning such as AI-powered language learning platforms as the most commonly adopted AI methods in education due to their effectiveness.”
- Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach (2021, neutral): “There is also a lack of systematic literature reviews and empirical Chen et al. (2020a) AIED bibliometric 9560 1999–2019 Multiple perspectives on the development of AIED in terms of the academic contexts the studies and researchers are situated in Chen et al. (2020b) AIED bibliometric 45 1999–2020…”
- Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, methodology): “…wearable devices, robot control, and sensing devices, and 5G wireless communication technologies, have provided new affordances and opportunities to integrate AI with collection and analysis of online learning processes in online higher education (Chen et al., 2020b; Hwang et al., 2020).”
- Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, background): “Next-generation educational technologies have led to the extensive applications of computers and information and related computing technologies in education, such as artificial intelligence in education (AIEd) (Chassignal et al., 2018; Chen et al., 2020; Zawacki-Richter et al., 2019).”
- 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).”
- Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “AI can support teachers in creating innovative assessment approaches (Chassignol et al., 2018; Chen et al., 2020a, 2020b).”