Published: Jan. 1, 2024
Language: Английский
Computers in Human Behavior, Journal Year: 2025, Volume and Issue: 166, P. 108569 - 108569
Published: Jan. 31, 2025
Language: Английский
Citations
0
International Journal of Educational Research Open, Journal Year: 2025, Volume and Issue: 8, P. 100452 - 100452
Published: Feb. 12, 2025
Language: Английский
Citations
0
Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3960 - 3960
Published: April 3, 2025
The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching learning CS, with an emphasis on education. Following the PRISMA methodology, categorizes according to instructional design models, roles, actions, benefits, challenges. Findings indicate that tools, particularly chatbots, intelligent tutoring systems, code generators, effectively personalized instruction, immediate feedback, interactive problem-solving across CS database-specific contexts. However, challenges persist, including inaccuracies, biases, student dependency AI, academic integrity risks. also identifies a shift programming as reshapes software development practices, prompting need align curricula evolving industry expectations. Despite growing attention education, database-related research limited. highlights necessity for further investigations specifically more extensive addressing AI-driven pedagogical strategies their long-term impacts. results suggest careful tools can complement traditional emphasizing critical role human educators achieving meaningful effective outcomes.
Language: Английский
Citations
0
Journal of Educational Computing Research, Journal Year: 2025, Volume and Issue: unknown
Published: April 17, 2025
Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative learning supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency students’ and development computational thinking. To address above issues, this study introduces generative AI into proposes dialogue-negotiated method based on AI. The focuses problems-solving process constructs multiple agents through Prompt design, enable students improve their thinking master skills in interaction for problem-solving. Finally, quasi-experiment was conducted verify effectiveness proposed 10th grade computer course high school. 43 experimental group learned method, while 42 control adopted computer-supported method. results showed that more significantly improved thinking, attitudes, achievement. This provides theoretical foundations application reference future AI-assisted teaching.
Language: Английский
Citations
0
Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown
Published: April 17, 2025
Abstract Understanding the emotions experienced by programming students, particularly concerning gender and education level, is increasingly critical. However, only limited research has used text data to examine these differences within context of emotions. This study aims determine students’ any based on in secondary higher compare performances algorithms prediction with sentiment analysis. The uses concurrent conversion mixed methods from two groups. first group consisted 444 school students who completed an electronic questionnaire created for this study. second comprised 202 first-year software engineering computer science students. results independent sample t-tests revealed significant enjoyment, anxiety, boredom, hope scores among gender. t-values each category were as follows: enjoyment (t = 2.333, p < .05), anxiety 2.519, boredom 3.841, .01), -3.829, .01). Among middle girls reported compared boys, while their lower. no statistical occurred between females males at levels. Sentiment analysis that BERTurk achieved accuracy than machine learning. BERT produced 96% 92% hope, 97% support vector machines random forest 94% predicting positive negative
Language: Английский
Citations
0
Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 143 - 168
Published: Sept. 13, 2024
This chapter explores the potential transformative benefits that generative AI could offer to developing nations. The presents concrete illustrations of how impacts economic development, education, and health care, while also offering prospects for environmental protection. In this chapter, we will explore two technologies: Generative Adversarial Networks (GANs) Transformers. delves into these other matters, elucidating each constructs by examining pros cons. It aims ensure certain stakeholders adopt comprehensive frameworks, facilitating discussions on regulation ensuring fair access all users technologies. These findings emphasise immediate requirement significant worldwide investments in education training equip future generations with necessary skills an economy driven artificial intelligence.
Language: Английский
Citations
0
Published: Jan. 1, 2024
Language: Английский
Citations
0