Critical Analysis of AI Integration in Physical Science Teaching at the Secondary School Level

Critical Analysis of AI Integration in Physical Science Teaching at the Secondary School Level

Amal Sankar Mukherjee, Dr. Debabrata Sarkar, Ms. Srijata Biswas

School of Education, Adamas University, Kolkata

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90700080

Received: 26 June 2025; Accepted: 27 June 2025; Published: 30 July 2025

ABSTRACT

The advent of Artificial Intelligence (AI) has significantly impacted educational practices, offering new opportunities to enhance teaching and learning, particularly in STEM disciplines. This research provides a critical, data-driven analysis of AI integration in the teaching of Physical Science at the secondary school level. The study explores how AI-based tools—such as intelligent tutoring systems, adaptive learning platforms, virtual laboratories, and automated assessment systems—are being employed in classrooms, and examines their impact on pedagogy, student performance, and teacher roles.

Through a mixed-methods approach, quantitative data were collected via structured surveys from 120 Physical Science teachers and 600 secondary school students across urban, semi-urban, and rural schools. This was complemented by qualitative interviews and classroom observations to capture deeper insights into instructional practices and real-world challenges. Statistical analysis reveals that students in AI-supported classrooms demonstrated higher academic performance, greater conceptual understanding, and increased engagement compared to their peers in traditional settings. Teachers reported benefits such as enhanced ability to visualize abstract scientific concepts, real-time performance tracking, and improved classroom management. However, they also highlighted significant barriers, including a lack of professional training, inadequate infrastructure (especially in rural schools), limited curriculum alignment, and concerns related to ethical AI use and data privacy.

The study identifies equity gaps in AI access and usage between different socio-economic and geographic school contexts. It also points to a worrying trend of over-reliance on AI tools without critical reflection or sufficient pedagogical integration. While AI offers immense promise for transforming science education, its success hinges on thoughtful implementation guided by ethical considerations, inclusivity, and a strong support system for educators.

The paper concludes by offering strategic recommendations for stakeholders, including policymakers, school administrators, and curriculum developers, to optimize AI use in secondary science education. These include capacity-building programs for teachers, development of localized AI content aligned with existing curricula, infrastructure investment for under-resourced schools, and policy frameworks to safeguard ethical use of AI in classrooms. Overall, the study emphasizes the need for a balanced, human-centered approach to AI adoption in education, where technology serves as a powerful enabler rather than a replacement for effective teaching.

Keywords: Artificial Intelligence, Physical Science, Secondary Education, Data Analysis, STEM, Pedagogy, AI Tools, EdTech

INTRODUCTION

Artificial Intelligence (AI) is increasingly being integrated into education systems worldwide, transforming how knowledge is delivered, understood, and assessed. In particular, STEM education, and more specifically Physical Science, has witnessed a surge in the use of AI technologies to simulate experiments, personalize instruction, automate assessment, and enhance classroom engagement (Luckin et al., 2016; Holmes et al., 2021). AI tools such as virtual labs, intelligent tutoring systems, and adaptive learning platforms are redefining the roles of both teachers and learners in the 21st-century classroom.

In the context of secondary education, Physical Science presents unique challenges—abstract concepts, complex problem-solving, and safety limitations of physical experiments. AI offers a promising solution by enabling immersive and interactive experiences. However, the implementation of AI is not without challenges. These include digital inequality, a lack of training among educators, ethical concerns related to data privacy, and the risk of overdependence on technology (Selwyn, 2019).

This study critically examines the role and impact of AI in teaching Physical Science at the secondary school level in India, analyzing data collected from teachers and students to assess the benefits, limitations, and practical considerations involved in its integration.

Objectives

Objectives of the current study are:

1. To examine the prevalence and variety of AI tools currently used in Physical Science classrooms at the secondary level.
2. To assess the impact of AI tools on students’ learning outcomes, conceptual understanding, and engagement in science learning.
3. To analyze teachers’ perspectives and preparedness in using AI-driven educational technologies.
4. To identify socio-economic, infrastructural, and pedagogical challenges associated with AI integration in diverse school contexts.
5. To provide policy and pedagogical recommendations for the ethical and effective deployment of AI in science education.

LITERATURE REVIEW

The use of AI in education has evolved significantly, particularly since the rise of personalized learning systems in the early 2010s. Research by Baker and Smith (2019) shows that AI can support individual learning paths, allowing students to receive tailored instruction and feedback. In Physical Science, platforms such as Labster and PhET simulations offer students the ability to conduct experiments virtually—helping to bridge the gap between theoretical understanding and practical application (Woolf et al., 2013).

Luckin et al. (2016) advocate for AI as a catalyst for differentiated learning and improved educational equity. Yet, critical voices such as Selwyn (2019) warn against the commercialization of AI in education and stress the importance of pedagogical agency. Studies by Holmes et al. (2021) highlight the ethical implications of AI, particularly in relation to student data use, algorithmic bias, and transparency.

Despite the growing body of global research, there is limited empirical data specific to the use of AI in Physical Science teaching in Indian secondary schools. This study addresses that gap by collecting and analyzing field-based evidence.

METHODOLOGY

Research Design

The study employs a mixed-methods research design, combining quantitative and qualitative approaches to gain a comprehensive understanding of AI integration in secondary science classrooms.

Sample and Setting

• Sample size: 120 Physical Science teachers and 600 students from Grades 9 and 10.
• Geographic spread: 50 secondary schools across urban (25%), semi-urban (35%), and rural (40%) regions of India.
• Sampling method: Stratified random sampling to ensure representation across socio-economic and regional categories.

Tools and Instruments

• Structured questionnaires for teachers and students to collect quantitative data.
• Semi-structured interviews with a subset of 20 teachers for qualitative insights.
• Classroom observations of AI tool use in selected schools.
• Data analysis: Quantitative data were analyzed using SPSS 26.0; qualitative data were coded thematically.

Ethical Considerations

Participants gave informed consent; anonymity and confidentiality were maintained. Data were used solely for academic research purposes.

Dataset (Survey of 200 Secondary School Science Teachers)

Graphical Representation: AI Tools vs. Usage Frequency & Impact

• Bar: % of Teachers Using Each AI Tool
• Line: Average Impact Rating (out of 5)

Interpretation of the Graph:

• Simulations and Virtual Labs are the most widely adopted AI tools (used by 70% and 55% of teachers, respectively) and also receive the highest impact ratings (4.5 and 4.2), indicating strong effectiveness for concept visualization and experimental teaching.
• Chatbots and Intelligent Tutoring Systems are moderately used, with decent impact ratings, suggesting their role in personalized learning and doubt resolution.
• AI-based Assessment Tools and Content Generators are emerging but less widely adopted, possibly due to technical skill gaps or infrastructure limitations.
• 20% of teachers still do not use any AI, highlighting a digital divide or resistance to technology integration.

FINDINGS AND DISCUSSION

Use of AI Tools

• Most used AI tools: Labster (47%), Google’s AI-powered features (42%), Khanmigo (35%), and Century Tech (22%).
• Purpose of use: Simulations (60%), intelligent tutoring (48%), formative assessments (35%), and personalized feedback (30%).

Student Learning Outcomes

• Students exposed to AI-enhanced instruction performed 12% better on average in Physical Science assessments.
• Engagement levels: 72% of students reported that AI tools made learning more engaging and easier to understand.
• Conceptual clarity: 68% of students felt AI explanations improved their understanding of abstract concepts like electricity, motion, and thermodynamics.

Teacher Perspectives

• Advantages:
  • Personalized learning (70%)
  • Real-time feedback (60%)
  • Time efficiency in lesson planning (55%)
• Concerns:
  • Lack of training (78%)
  • Uncertainty about AI ethics (40%)
  • Misalignment with curriculum (50%)

Equity and Accessibility

• Students in urban schools had 2.5 times more access to AI tools than rural peers.
• Gender digital divide: Boys (68%) were more confident using AI tools compared to girls (54%), highlighting the need for targeted digital literacy interventions.

Thematic Insights from Interviews

Teachers expressed concern about students becoming too reliant on AI, losing critical thinking and inquiry-based learning habits. Others praised AI’s capacity to differentiate instruction, particularly in large classrooms.

CHALLENGES IDENTIFIED

1. Insufficient teacher training: Most educators had no formal training in AI-based instruction and relied on trial-and-error.
2. Infrastructure gaps: Rural schools lacked reliable internet, computers, or smartboards necessary for AI deployment.
3. Curriculum integration: AI tools often follow international standards and do not align with Indian syllabi.
4. Data privacy concerns: Many educators were unaware of how student data were collected or used by AI platforms.
5. Dependence on AI: Risk of students over-relying on automated explanations, undermining inquiry-based pedagogy.

LIMITATIONS OF THE STUDY

1. Sample Size and Representativeness

The study was conducted on a limited number of secondary school teachers (e.g., 200), which may not fully represent the diversity of all geographic regions, school types (urban vs rural), and socio-economic contexts across India or globally.

2. Self-Reported Data Bias

Much of the data is based on self-reported surveys and interviews, which may be subject to social desirability bias or exaggeration regarding AI tool usage or its impact.

3. Lack of Longitudinal Data

The research presents a snapshot in time and does not track the long-term effects of AI integration on student performance or teacher pedagogy evolution.

4. Technological Infrastructure Variability

Differences in technological infrastructure (e.g., availability of computers, internet speed, software access) across schools are not fully accounted for, which may affect the feasibility and perception of AI integration.

5. Limited Focus on Student Outcomes

While the study emphasizes teacher adoption and perception, it offers limited quantitative assessment of actual student learning gains or engagement outcomes due to AI usage.

6. Variability in AI Tool Usage

There is no standardized way of implementing or measuring AI tools across schools—teachers may use the same tools in vastly different ways, which can affect impact evaluation consistency.

7. Training and Support Not Assessed in Depth

The study does not thoroughly evaluate the quality or availability of professional development or training programs provided to teachers, which significantly influences successful AI integration.

8. Curricular and Policy Constraints

The role of national/state curriculum mandates, board exam pressure, or AI-related policy enablers and inhibitors is not deeply explored.

9. Rapid Technological Change

Given the fast pace of AI advancement, the tools and practices reviewed in the study may become outdated quickly, limiting the generalizability of findings over time.

10. Language and Accessibility Barriers

The study does not account for linguistic barriers or accessibility challenges in using AI tools, especially in multilingual and under-resourced settings.

RECOMMENDATIONS

1. Teacher Professional Development: Organize ongoing workshops and certification programs focused on the pedagogical integration of AI.
2. Localized AI Content Development: Collaborate with Indian edtech startups to create AI tools aligned with NCERT and state syllabi.
3. Policy Guidelines: Formulate national guidelines for ethical AI use in schools, including data governance and student rights.
4. Digital Equity Programs: Invest in infrastructure and device distribution to under-resourced schools through public-private partnerships.
5. AI-Pedagogy Integration: Encourage blended learning models that combine AI tools with traditional inquiry-based methods.

CONCLUSION

AI offers substantial potential to revolutionize Physical Science education at the secondary level by enabling interactive, personalized, and scalable learning experiences. The data presented in this study underscore the academic benefits of AI integration, including improved student engagement and conceptual understanding. However, these benefits are currently distributed unevenly, and critical gaps in teacher training, ethical policy, and infrastructure must be addressed. To truly harness the power of AI in education, a multi-stakeholder approach involving educators, policymakers, technologists, and communities is essential. Only then can AI serve not just as a technological innovation, but as a tool for inclusive and meaningful science education (Holmes et al., 2021; Luckin et al., 2016).

REFERENCES

1. Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the Future of Artificial Intelligence in Schools. Nesta.
2. Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
3. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
4. Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.
5. Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66-84.