Artificial intelligence in science and chemistry education: a systematic review
AI Analysis: Artificial intelligence in science and chemistry education: a systematic review
There is increasing interest in the use of artificial intelligence technologies, which have become a transformative force in the field of education, in educational fields that include subject areas that support lifelong learning, such as science and chemistry education. In this study, a systematic review was conducted on the use and effects of artificial intelligence applications in science and chemistry education between 2014 and 2024. As a result of the systematic review, it was seen that there was an increase in studies between 2021 and 2024. It was seen that artificial intelligence applications are mostly carried out in the field of science education. ChatGPT and conversational robots are usually used. These tools mostly impact on learning outcomes related to the learning process and researchers mostly report risks/limitations related to ethical issues in the use of Al tools in education. These applications are seen to have effects such as contributing to the online learning process of students, facilitating learning, providing multi-modal (auditory) learning environments for science/chemistry laboratory courses, providing interdisciplinary learning experiences, and encouraging personalized learning. In the reviewed studies, researchers generally emphasize ethical challenges and limitations regarding gender and racial bias, hallucinations, copyright infringement, plagiarism and biased information production, issues related to accuracy and reliability, problems with technical infrastructure and language support, and the impact of these practices on human decision-making processes and writing skills. As a result, determining and presenting ethical issues regarding the use of artificial intelligence tools in science and chemistry education and increasing the awareness of students and teachers about the conscious use of these tools will be an important step.
Executive Impact Summary
This systematic review reveals a significant uptick in AI research within science and chemistry education, particularly from 2021-2024. The findings highlight AI's capacity to personalize learning, enhance engagement, and streamline administrative tasks. However, critical challenges, including ethical concerns around bias, plagiarism, and data accuracy, necessitate careful consideration and strategic integration. Our analysis provides actionable insights for educators and institutions looking to leverage AI responsibly.
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Growth in AI Studies (2021-2024)
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Science Education AI Focus
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ChatGPT/Chatbot Use
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Ethical Concerns Reported
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
- Increase in studies between 2021 and 2024.
- Predominance of AI applications in science education over chemistry.
- Widespread use of ChatGPT and conversational robots.
- Contribution to online learning processes.
- Facilitation of learning and knowledge construction.
- Provision of multi-modal learning environments (auditory for labs).
- Encouragement of personalized and interdisciplinary learning.
- Development of prompt writing skills and self-confidence.
- Increased student participation.
- Gender and racial bias, hallucinations in AI-generated content.
- Copyright infringement and plagiarism concerns.
- Issues with accuracy, reliability, and biased information production.
- Technical infrastructure and language support problems.
- Negative impact on human decision-making and writing skills.
- Over-reliance on AI tools by students.
- Development of teacher candidates' evaluation expertise and responsive teaching skills.
- Assistance in classroom management and lesson plan design.
- Increased acceptance of AI technologies among teachers and students.
2024
Year with the highest number of AI in Education studies identified, indicating rapid growth.
Enterprise Process Flow
Initial Search (255 records)
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Duplicate Removal (106 records)
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Initial Screening (149 records)
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Exclusion (117 records: qualitative, reviews, non-English, off-topic)
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Retrieval Attempt (32 reports)
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Reports Not Retrieved (7 reports)
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Eligibility Assessment (25 reports)
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Exclusion (7 reports: framework, assistive AI)
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Included Studies (18 reports)
| AI Benefit | Science Education Impact | Chemistry Education Impact |
|---|---|---|
| Personalized Learning |
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| Content Creation |
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| Assessment |
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Ethical Challenges in AI Integration
Problem: Researchers identified significant ethical challenges, including gender and racial bias, hallucinations, copyright infringement, and plagiarism concerns in AI-generated content. These issues can lead to discrimination and academic integrity concerns.
Solution: Recommendations include incorporating AI literacy into TPACK framework, critical perspective on AI use, necessary precautions, and specific teacher training on ethical implications.
Impact: Mitigates risks of biased information, promotes responsible AI use, and upholds academic integrity in science and chemistry education.
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Your AI Implementation Roadmap
A strategic phased approach to integrating AI, from literacy to advanced research, ensuring responsible and effective adoption.
Phase 1: AI Literacy and Awareness Programs
Develop and implement training programs for teachers and students focusing on conscious and ethical AI use. Address issues like bias detection, plagiarism, and responsible information verification. Emphasize AI's role as a tool, not a replacement for critical thinking.
Phase 2: Pedagogical Integration Workshops
Organize workshops for science and chemistry educators to explore practical AI applications in lesson planning, assessment design, and creating personalized learning experiences. Focus on using AI to support conceptual understanding and scientific process skills, especially for abstract topics.
Phase 3: Technical Infrastructure and Support
Evaluate and upgrade technical infrastructure to ensure seamless integration of AI tools. Address language compatibility issues and improve AI system accuracy. Develop support systems for educators facing technical challenges.
Phase 4: Research and Development
Initiate further experimental and mixed-method research focusing on AI's impact on conceptual learning and science process skills at various educational levels (high school, middle school). Investigate AI's role in teacher professional development and techno-pedagogical competencies.
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