📄 social_science

Zambian Secondary School Teachers’ TPACK and their Attitude Towards Artificial Intelligence in Mathematics Classrooms: A Case of Mufulira District

This study of 127 mathematics teachers in Mufulira District, Zambia, reveals that while TPACK is a vital competency, it does not significantly influence teachers' attitudes toward adopting Artificial Intelligence in mathematics classrooms, suggesting that policymakers should focus more on addressing best practices to shift general perspectives on AI integration.

Original authors: Donald Siame, Allan Musonda

Published 2026-07-08

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Donald Siame, Allan Musonda

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: A Recipe for the Future

Imagine teaching mathematics is like cooking a complex meal. To make a great dish, a chef needs three things:

The Ingredients (The Math content).
The Cooking Techniques (Pedagogy/How to teach).
The Kitchen Tools (Technology).

In the academic world, the combination of these three is called TPACK (Technological Pedagogical Content Knowledge). If a teacher has high TPACK, they are like a master chef who knows exactly which tool to use for which ingredient to create the perfect meal.

Now, a new, powerful kitchen gadget has arrived: Artificial Intelligence (AI). It can chop vegetables, suggest recipes, and even taste the food. But, just because you have a fancy new gadget doesn't mean you want to use it, or that you know how to use it well.

This study, conducted in the Mufulira district of Zambia, asked a simple question: Do teachers who are already "master chefs" (high TPACK) automatically want to use this new AI gadget?

The Setup: Who Was Asked?

The researchers went to 127 math teachers in secondary schools. They gave them a survey (like a long checklist) and then sat down with 10 of them for a chat (interviews). They wanted to see if the teachers' skill level with technology predicted their feelings about using AI in math class.

The Findings: The Surprising Twist

1. The "Tool" vs. The "Will"
The researchers expected to find a strong link: If a teacher is good with tech, they will love AI.
The Reality: It was like finding that owning a high-end sports car doesn't necessarily make someone want to drive a race car.

The Data: The study found almost no connection between a teacher's TPACK skills and their attitude toward AI.
The Metaphor: Imagine two people. One is a mechanic who knows exactly how to fix a car (High TPACK). The other is someone who barely knows how to turn a key (Low TPACK). The study found that the mechanic isn't necessarily more excited to drive the car than the person who doesn't know how to start it. Their skill didn't predict their enthusiasm.

2. What Teachers Actually Thought

The Good News: Most teachers (about 69%) believe AI can help students understand math better. About 57% think it can fix the problem of students doing poorly in math.
The Bad News: Many teachers are worried. About 27% think AI tools are too complicated to use in a classroom.
The "Trust" Issue: In the interviews, teachers said things like, "I don't trust it; it gives wrong answers," or "It's tricky." They feel that while AI is relevant, they don't know how to use it properly yet.

3. The "Workload" Dilemma
Teachers believe AI could reduce their workload (like a dishwasher that cleans the plates for you). However, they are worried that if they use it too much, they might lose the personal connection with their students (like a dishwasher that makes the kitchen feel cold and impersonal).

The Conclusion: It's About Mindset, Not Just Skill

The paper concludes with a very important distinction.

Old Belief: "If we just train teachers to be better with technology (raise their TPACK), they will naturally start using AI."
New Finding: "No, that's not how it works."

The study suggests that attitude is more important than skill when it comes to adopting AI. A teacher might be a tech wizard (high TPACK) but still refuse to use AI because they think it's dangerous, useless, or too hard. Conversely, a teacher with less tech skill might be eager to try it if they believe in its value.

The Final Takeaway

The authors are telling policymakers and school leaders: Don't just focus on training teachers on how to use the tools. You also need to change their mindset. You have to show them why it's useful and how to use it safely, otherwise, even the most skilled teachers won't want to bring the new gadget into their classroom.

In short: Having the right tools (TPACK) doesn't guarantee you'll want to use the new gadget (AI). You need to change the teacher's perspective first.

Technical Summary: Zambian Secondary School Teachers' TPACK and their Attitude Towards Artificial Intelligence in Mathematics Classrooms

Problem Statement
While the global educational landscape is rapidly integrating Artificial Intelligence (AI), and the Zambian Ministry of Technology and Science has identified a need for a skilled AI workforce, there is a lack of empirical research within Zambia regarding the intersection of teachers' technological competence and their acceptance of AI. Specifically, it remains unclear whether Zambian secondary school mathematics teachers' Technological Pedagogical Content Knowledge (TPACK) levels serve as a determining factor for their attitudes and intentions toward adopting AI in mathematics classrooms. Although international literature suggests a correlation between TPACK and technology adoption, the specific dynamics in the Zambian context, where AI presents both potential benefits (e.g., personalized learning) and risks (e.g., diminished critical thinking), require investigation to inform policy and curriculum development.

Methodology
The study employed a sequential embedded mixed-methods research design within the Mufulira District of Zambia.

Participants: The quantitative sample consisted of 127 secondary school mathematics teachers (out of a population of ~200), representing a 94.8% response rate. The qualitative component involved 10 teachers selected from three schools for semi-structured interviews.
Instrumentation: Data collection utilized an online questionnaire (Google Forms) for quantitative data and semi-structured interviews for qualitative insights. The questionnaire measured TPACK levels (specifically the alignment of technology with mathematics content) and attitudes toward AI (perceptions of utility, complexity, and impact on problem-solving).
Analysis: Quantitative data were analyzed using descriptive statistics (frequencies, means) and Pearson correlation analysis to determine the strength and significance of the relationship between TPACK levels and attitudes toward AI. Qualitative data were analyzed thematically to explore teachers' perceptions and the nuances of their adoption barriers.
Ethical Considerations: The study adhered to strict ethical protocols, including informed consent, anonymity, and the right to withdraw.

Key Results
The study yielded several critical findings regarding the relationship between TPACK and AI adoption:

General Perceptions: A majority of teachers (69.05%) believe AI can improve pupils' understanding of mathematics, and 57.6% agree it could help improve poor performance. Similarly, 59.06% believe AI enhances problem-solving skills. However, a significant portion (27.2%) perceives AI tools as too complex for classroom use.
Statistical Relationship (TPACK vs. Attitude): Contrary to the hypothesis that higher TPACK levels drive positive AI adoption, the Pearson correlation analysis revealed no statistically significant relationship between teachers' TPACK levels and their attitudes toward AI.
- Improvement of Understanding: $r = 0.109$ , $p = 0.225$ (Very weak, non-significant).
- Enhancement of Problem-Solving: $r = -0.028$ , $p = 0.997$ (Negligible, non-significant).
- Complexity of Tools: $r = -0.090$ , $p = 0.321$ (Very weak, non-significant).
- Improvement of Performance: $r = 0.046$ , $p = 0.609$ (Negligible, non-significant).
- Overall Perception: $r = 0.087$ , $p = 0.331$ (Very weak, non-significant).
Qualitative Insights: Interviews revealed that while many teachers acknowledge the relevance of AI, very few actively use it due to a lack of knowledge on integration strategies. A divergence in opinion exists: some confident teachers believe TPACK facilitates AI adoption, while others argue that even high TPACK does not guarantee adoption if the teacher holds negative beliefs about AI's safety or utility.

Key Contributions and Significance
The primary contribution of this study is the empirical finding that TPACK levels are not a strong predictor of teachers' attitudes toward AI adoption in the Zambian mathematics context. The authors challenge the assumption that technical competence alone drives acceptance.

Shift in Focus: The study suggests that while TPACK is vital for the implementation of technology, it is less influential on the attitude or intention to adopt AI.
Policy Implication: The authors argue that policymakers and curriculum developers should shift their focus from merely assessing technical skills to addressing the "general perspective" of teachers. The significance lies in the need to provide best practices and training that specifically address the why and how of AI usage to change negative perceptions, rather than assuming that increasing TPACK will automatically result in positive adoption.
Contextual Relevance: The study highlights that in the Zambian context, the barrier to AI integration is not necessarily a lack of digital skills (TPACK) but rather a lack of understanding regarding the specific application of AI tools and the need to balance their use to avoid negatively affecting teacher-pupil interaction and critical thinking.

Conclusion
The paper concludes that while TPACK is a foundational element for technology integration, it has "less to do" with a teacher's attitude toward adopting AI in mathematics. The study emphasizes that attitude is a more critical determinant than TPACK levels in this specific domain. Consequently, successful integration requires targeted interventions that address teacher beliefs and provide concrete strategies for AI usage, rather than relying solely on technical upskilling.

The Big Picture: A Recipe for the Future

The Setup: Who Was Asked?

The Findings: The Surprising Twist

The Conclusion: It's About Mindset, Not Just Skill

The Final Takeaway

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