MoyoEd Research

Introduction.

Artificial intelligence (AI) tools, from large language models (LLMs) to intelligent tutoring systems (ITS) and virtual laboratories, have moved from curiosity to classroom staple. In A-level Chemistry, where students must integrate theory, experimentation, and problem-solving under exam pressure, AI offers meaningful assistance. Teachers can harness it for faster feedback, targeted practice, and improved conceptual visualisation.

However, its use must be balanced. Although AI can enhance understanding and engagement, uncritical adoption poses risks of errors, bias, or dependence (Chaudhry et al., 2022). The key is not automation but augmentation—using AI to strengthen the teacher’s role and the learner’s reasoning (Wang, 2024).

1. Current Challenges in A-Level Chemistry

A-level Chemistry requires both conceptual depth and procedural accuracy. However, teachers face persistent issues.

• Abstract concepts: Misconceptions regarding atomic structure, bonding, or equilibria.
• Complex problem-solving: Multi-step numerical and algebraic challenges.
• Limited laboratory access: Time, safety, and cost constraints.
• Feedback bottlenecks: Manual marking delays hinder the learning cycles.
• Academic integrity concerns: Copy-paste risks with the use of generative AI.

Such issues often lead to rote memorisation rather than chemical reasoning (Chan, 2021).

2. How AI Can Help

– Intelligent tutoring systems (ITS) adapt question difficulty and hint delivery to individual learners, thereby improving conceptual responses (King et al., 2022).
– NLP-based feedback tools evaluate student explanations and suggest refinements that deepen reasoning (Demszky & Liu, 2023).
– Virtual labs safely simulate practicals, helping students visualise particle interactions and experimental variables (Chan, 2021; Bazie, 2024).
– Generative AI (e.g., ChatGPT) can produce custom problem sets and worked examples, which are ideal for formative assessment if teachers vet their accuracy (Wang, 2024).

3. What Research Shows (2018–2025)

1. King et al. (2022)—Open-Response Chemistry Tutor enhanced student reasoning in free-response tasks.
2. Chan (2021)—Virtual labs improved conceptual understanding as effectively as some physical labs.
3. Demszky and Liu (2023)—NLP feedback improved teacher efficiency and student engagement.
4. Diéz-Pascual et al. (2022)—Remote practical redesigns maintained lab learning outcomes during COVID.
5. Heeg (2023) — AI in science classrooms boosts motivation but requires ethical oversight.

4. Practical Classroom Strategies

1. Align AI use with objectives: Choose a focus area (e.g., feedback on laboratory reports).
2. Use of AI for formative feedback: Rapid, individualised guidance helps learners self-correct.
3. Blending virtual and hands-on labs: Simulations prepare students for real experiments.
4. Teach prompt literacy: Model how to refine prompts, critique answers, and cross-check facts.
5. Assess process over product: Require reflection on how AI was used to solve problems.

5. Ethics and Assessment Integrity

Teachers must explicitly address the following:

• Plagiarism and dependence: Design assessments that value reasoning and explanation.
• Accuracy and safety: Validate AI-generated laboratory protocols.
• Bias and access: Ensure inclusivity in AI-supported tasks.
• AI literacy: Teach students how models generate answers and where errors arise (Heeg, 2023).

Conclusion

AI is not a substitute for good teaching; rather, it amplifies it. Used wisely, tools like ChatGPT and Bard extend teachers’ reach, giving students richer feedback, more practice, and deeper conceptual insights. The future of chemistry teaching lies in this synergy: human expertise guided by evidence and enhanced by intelligent technology (Wang, 2024; King et al., 2022).

References.

• Bazie, H. (2024). The effect of virtual laboratories on academic achievement in undergraduate chemistry. JMIR Formative Research.
• Chan, P. (2021). Virtual chemical laboratories: A systematic review. Computers & Education.
• Demszky, D., & Liu, J. (2023). M-Powering Teachers: NLP-Powered Feedback for Education. Working Paper.
• Diéz-Pascual, A. M., et al. (2022). Remote teaching of chemistry laboratories during COVID-19. Journal of Chemical Education.
• Heeg, D. M. (2023). AI in school science: Benefits and challenges. British Journal of Educational Technology.
• King, E. C., et al. (2022). Open-Response Chemistry Cognitive Assistance Tutor (ORCA). Journal of Chemical Education.
• Wang, S. (2024). Artificial intelligence in education: A systematic review. Computers & Education.