Enterprise AI Analysis
AI integration in science education classrooms: insights from preservice teachers on application, support, and training for multilingual learners
This mixed-methods study investigates preservice science teachers' perceptions of AI adoption to support multilingual learners (MLLs) in science classrooms. It evaluates their attitudes, training needs, and barriers. Quantitative and qualitative data reveal that most preservice teachers favor comprehensive hands-on AI training, from practical classroom applications to ethical data management, to make them confident and competent. Findings suggest a need for comprehensive training in both practical AI applications and ethical considerations to leverage AI's potential for equitable science education.
Executive Impact Summary
Key findings highlight the critical areas where AI can drive significant improvements in educational outcomes and teacher preparedness.
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Positive Perceptions & Attitudes Towards AI
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Recognized Importance of AI Training
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Agreement: AI's Role in Teaching & Development
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Limited Prior AI Training Experience
Deep Analysis & Enterprise Applications
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71.76%
Overall positive perception of AI integration
Preservice science teachers generally held moderately positive perceptions of AI integration in science education, with an overall mean agreement of 71.76%. Item 1 ('AI will play an important role in teaching and development in science education') received the highest agreement (80.42%), reflecting strong recognition of AI's future relevance for teaching multilingual learners in science classrooms.
However, perceptions were tempered by caution and ambivalence. For example, Item 2 ('Some teaching roles in science may be replaced by AI') scored moderately (60.83%), indicating concern about AI diminishing teacher-student interaction. Confidence in using AI and comfort with AI-related terminology (Items 3 and 4) showed lower agreement (~65%), suggesting teachers felt unprepared to use AI effectively.
Challenges in AI Integration for MLLs in Science
Dense Technical Vocabulary
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Abstract Concepts
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Inquiry-based Procedures
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Difficulty with AI Terminology
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Limited Technical Confidence
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Ethical & Professional Concerns
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Readiness Gap (Perception vs. Skill)
| Challenge | AI Opportunity |
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Preservice teachers identified clear opportunities for integrating AI into multilingual science classrooms. AI can extend beyond language support to address core features of science education, including engagement with inquiry practices, visualization of abstract concepts, and personalization of learning. They envision AI as a partner in scientific research, a tool for dynamic simulations, and a means of tailoring instruction while improving efficiency.
70.90%
Strong consensus on importance of AI training
Students acknowledged the importance of AI in science education, felt quite capable of using AI, and were beginning to become aware of its limitations and ethical implications. However, their score of only around 57% reflects a lack of strong acceptance of AI—ambivalent attitudes exist, particularly regarding understanding technical concepts and confidence in the accuracy of AI use.
The Need for Structured AI Training
Most students have never participated in formal training such as classes, courses, or workshops on the use of AI in educational contexts. This discrepancy between recognized needs and actual experiences suggests the urgency and importance of structured training programs. Without such intervention, students' optimism and critical awareness will not fully develop into real competencies when they face the future demands of multilingual science classrooms.
Source: Arini et al., 2026
Preservice teachers express concerns regarding their ability to implement AI effectively due to a lack of formal training, particularly in ensuring accurate real-time translations of scientific terminology and using AI-generated content without distorting scientific meaning. They highlighted the need for hands-on AI training that focuses on pedagogical application in multilingual science classrooms, ethical data management, and culturally inclusive approaches.
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Implementation Roadmap
Our tailored AI integration roadmap ensures a smooth transition, from foundational knowledge to ethical application and continuous improvement.
Foundational AI Knowledge
Teacher candidates develop a basic understanding of AI concepts, including natural language processing, adaptive systems, and machine learning, allowing them to critically assess how tools operate and what data they rely on.
Pedagogical Application Training
Training focuses on using AI tools (translators, glossaries, visual simulations) to scaffold complex science content for MLLs, integrating these tools into science inquiry and content instruction.
Ethical & Data Privacy Considerations
Development of an ethical framework for AI tool use, including student data protection, awareness of algorithmic bias, and strategies for culturally responsive AI integration.
Hands-on Practice & Reflective Engagement
Opportunities to use AI tools in simulated or real teaching contexts, structured reflection, peer feedback, and guided exploration to support critical thinking about AI's value and limitations.
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