A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

Tan, Seng Chee; Lee, Alwyn Vwen Yen; Lee, Min

doi:10.1016/j.caeai.2022.100097

Cited by 40 publications

(20 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The collective performance and learning content are subcategories defined by Chee et al (2022). The category of individual performance emerged in the development of this systematic review.…”

Section: How Results Are Analyzedmentioning

confidence: 99%

“…The line of work could be to explore the participants' feelings and the interaction that occurs in learning environments; then, it would be necessary to assess the collaboration achieved between students and teachers through how they interact and finally to evaluate the development of competencies. Discourse patterns are another advanced topic in AI, as discussed by Chee et al (2022) and Dowell et al (2019) but developing competencies through collaboration is not yet sufficiently addressed.…”

Section: Discussionmentioning

confidence: 99%

“…The analysis of the 30 selected articles identified three categories that deserve attention: the objects of study, how the results are analyzed, and the types of AI that could be used. These categories were proposed by Chee et al (2022).…”

Section: Regarding the Second Question Of This Systematic Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review

et al. 2023

View full text Add to dashboard Cite

The diversity of topics in education makes it difficult for artificial intelligence (AI) to address them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The analysis of competency development by fostering collaboration should be one of them because competencies are the way to validate that the educational exercise has been successful and because collaboration has proven to be one of the most effective strategies to improve performance outcomes. This systematic review analyzes the relationship between AI, competency development, and collaborative learning (CL). PRISMA methodology is used with data from the SCOPUS database. A total of 1,233 articles were found, and 30 passed the inclusion and exclusion criteria. The analysis of the selected articles identified three categories that deserve attention: the objects of study, the way of analyzing the results, and the types of AI that could be used. In this way, it has been possible to determine the relationship offered by the studies between skill development and CL and ideas about AI’s contributions to this field. Overall, however, the data from this systematic review suggest that, although AI has great potential to improve education, it should be approached with caution. More research is needed to fully understand its impact and how best to apply this technology in the classroom, minimizing its drawbacks, which may be relevant, and making truly effective and productive use of it.

show abstract

Section: How Results Are Analyzedmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Another significant aspect of AI in education is the availability of AI-powered collaborative learning platforms (Ramadevi et al, 2023), which facilitate peer-to-peer learning and cooperation through enhanced group dynamics analytics (Calvo et al, 2011; Diziol et al, 2010). Indeed, different studies focus on spacing students in optimal groups (Elghomary et al, 2022; Kumar & Rosé, 2011) and offer educators specific recommendations on instructional strategies for collaborative learning (Casamayor et al, 2009; Tan et al, 2022). Meanwhile, AI's role in supporting teachers’ professional development (Lampos et al, 2021; Li & Su, 2020) appears to be a promising future avenue.…”

Section: Literature Reviewmentioning

confidence: 99%

The Transformative Trajectory of Artificial Intelligence in Education: The Two Decades of Bibliometric Retrospect

Kavitha,

Joshith

2024

Journal of Educational Technology Systems

View full text Add to dashboard Cite

Integrating artificial intelligence (AI) stands out as the most dynamic and innovative breakthrough in introducing disruptive paths in the varied domains of education. This bibliometric analysis delved into the trajectory of AI’s evolving landscape within educational settings over two decades, encompassing 324 articles published from 2003 to 2023, sourced from the Scopus database. The study uncovers a substantial surge in publications with a steep increase from 2020, peaking in 2023. Notably, while established nations like China and the US lead in publications, notable contributions from other developing countries, including Saudi Arabia, India, and Malaysia, underscored a global shift. Key terms, including students, machine learning, AI and higher education, underpin the central focus research areas while emerging themes like “generative AI” and chatbots like “chatgpt” mark the evolving trends. Further, the study prompts sustained global partnerships, interdisciplinary collaborations, and continued exploration of emerging AI technologies to catalyze educational advancements.

show abstract

“…The estimation of mutual inductance is a highly nonlinear and complex task to perform with mathematical models. As cited in [24], ML models are reliable as long as the data samples available appropriately represent the end-to-end relationship, whereas DL networks yield exceptional performance in classification and regression tasks [36] without the constraints of ML models [37] since they have the ability to adapt. With that said, it was decided to implement Multi-Layer Perceptron (MLP), i.e., a fully connected multi-layer neural network [38] in MATLAB ® software R2021b, using the fitrnet function.…”

Section: Developed Ann Modelmentioning

confidence: 99%

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

Abrantes,

Costa,

Perdigão

et al. 2024

Energies

View full text Add to dashboard Cite

In the context of inductive power transfer (IPT) for electric vehicle (EV) charging, the precise determination of the mutual inductance between the magnetic pads is of critical importance. The value of this inductance varies depending on the EV positioning, affecting the power transfer capability. Therefore, the precise determination of its value yields various advantages, particularly by contributing to the optimization of the charging process of the EV batteries, since it offers the possibility of adjusting the position of the vehicle depending on the level of misalignment. Within this framework, algorithms grounded in artificial intelligence (AI) techniques emerge as promising solutions. This research work revolves around the estimation of the mutual inductance in a wireless inductive power transfer system using a resonant converter topology, implemented in MATLAB/Simulink® R2021b. The system output was developed to emulate the behavior of a battery charger. To estimate this parameter, an artificial neural network (ANN) was developed. Given the characteristics of the system, the features were chosen in a way that they could provide a clear indication to the ANN if the vehicle position changed, independently of the charging power. In the pursuit of creating a robust AI model, the training dataset contained approximately 1% of the available data. Upon the analysis of the results, it was verified that the largest estimation error observed was around 3%, occurring at the lowest charging power considered. Hence, it can be inferred that the proposed ANN exhibits the capability to accurately estimate the value of mutual inductance in this type of system.

show abstract

A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

Cited by 40 publications

References 62 publications

Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review

Collaborative learning and skill development for educational growth of artificial intelligence: A systematic review

The Transformative Trajectory of Artificial Intelligence in Education: The Two Decades of Bibliometric Retrospect

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

Contact Info

Product

Resources

About