The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research

Dindar, Muhterem; Muukkonen, Hanni; Järvelä, Sanna

doi:10.1007/s11528-022-00715-y

Cited by 179 publications

(103 citation statements)

References 77 publications

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“…These particulars impact how education is delivered and received today enormous (Pereira et al, 2022;Celik et al, 2022). To positive use, AI-based tools and systems can personalize learning experiences, improve teacher productivity and enhance student attention.…”

Section: / 17mentioning

confidence: 99%

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

et al. 2023

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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.

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Section: / 17mentioning

confidence: 99%

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

et al. 2023

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“…When it comes to learning, Mikropoulos and Natsis point out that modelling and related tools and methods give students the hands-on experience and experiential learning they need, which enhances the quality of their education. The research they cited in their article also points out how VR and 3-D technology can enhance education by enhancing usability and student enjoyment (Celik et al, 2022;S. Raj, 2019).…”

Section: Learningmentioning

confidence: 99%

The Impact of AI on Teaching and Learning in Higher Education Technology

2022

JHETP

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Thanks to AI, students may now study whenever and wherever they like. Personalized feedback on assignments, quizzes, and other assessments can be generated using AI algorithms and utilised as a teaching tool to help students succeed. This study examined the impact of artificial intelligence in higher education teaching and learning. This study focuses on the impact of new technologies on student learning and educational institutions. With the rapid adoption of new technologies in higher education, as well as recent technological advancements, it is possible to forecast the future of higher education in a world where artificial intelligence is ubiquitous. Administration, student support, teaching, and learning can all benefit from the use of these technologies; we identify some challenges that higher education institutions and students may face, and we consider potential research directions.

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“…(a) Optimization model of dynamic innovation strategy is as follows: Reasonable judgment of dynamic innovation strategy is the main index to measure the artificial intelligence algorithm. e dynamic optimization among the production set operations, such as leading, assisting, and adjusting, can not only balance the relationship between the global judgment and the local judgment of the production set but also improve the corresponding judgment ability Buendgens [20]. From formula (8), we can see that in the early judgment of film and television works, we pay a great deal of attention to the global production ability, and in the later judgment, we pay attention to the local judgment, so different production sets should dynamically judge the judgment strategy according to the conditions.…”

Section: Dynamic Innovation Strategy Analysismentioning

confidence: 99%

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Han

Shen

2022

Mobile Information Systems

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In the process of choosing the best scheme in the artificial intelligence algorithm, it is impossible to accurately judge the nonlinear relationship between the innovation strategy and the film and television postproduction scheme. An improved artificial intelligence algorithm based on the integration of dynamic factors and the artificial intelligence algorithm is proposed to reduce the disturbance ability of the artificial intelligence algorithm and improve the analysis level of film and television postproduction and innovation strategy. Firstly, the initial innovation strategy set of the production set is established by using dynamic factors, which makes it discrete and reduces the influence of the scheme selection error on the results. Then, the production set is divided into dynamic subproduction sets by using the film and television production theory, and each subproduction set seeks its own parallel innovation strategy. Finally, under the guidance of film and television production theory, each subproduction set shares the matching of optimal solutions. Through MATLAB simulation analysis and verification, the improved dynamic artificial intelligence algorithm can improve the accuracy of judging the innovation strategy of film and television works in an uncertain environment and shorten the convergence time of global feature solution and is superior to the original selection method of film and television production strategy. In addition, under that condition the initial weight scheme and the threshold scheme are set. The artificial intelligence algorithm is used to analyze the innovation strategy selection of youth idol works. The results show that under different film and television production requirements, the innovation strategy selection judgment of the artificial intelligence algorithm is accurate and superior to the original film and television production strategy selection method, which further verifies the effectiveness of the artificial intelligence algorithm proposed in this paper.

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The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research

Cited by 179 publications

References 77 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 Impact of AI on Teaching and Learning in Higher Education Technology

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

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