AI-Powered Skill Gap Analysis and the Discovery of Teaching Coaches: Toward Evidence-Based Teacher Development

AI-Powered Skill Gap Analysis and the Discovery of Teaching Coaches: Toward Evidence-Based Teacher Development

Introduction

Teacher performance evaluation has traditionally relied on classroom observations, student course outcomes, and supervisor’s feedback and assessment. While these methods offer valuable insights, they often suffer from subjectivity, inconsistency, and limited scope. As educational institutions embrace data-informed practices, AI-powered skill gap analysis has emerged as a transformative tool capable of identifying teacher competencies, performance gaps, and leadership potential. As Hotwani (2025) explains, machine learning models now enable organizations to “gather and analyze data from examinations, performance evaluations, LMS records, and even work items”, providing targeted feedback and individualized learning pathways. This approach can equally benefit educational settings by enhancing teacher growth and helping institutions identify emerging teaching coaches and supervisors.

Literature Review: AI in Skill Gap and Teacher Evaluation

Recent advances in AI applications to teacher evaluation illustrate the potential of data-driven systems to offer a more objective, continuous, and personalized approach. Almubarak, Alhalabi, Albidewi, and Alharbi (2025) proposed a deep-learning model capable of analyzing classroom video data to assess teacher–student interactions, demonstrating how such systems “provide more consistent and scalable measures of instructional performance” (p. 2). Similarly, the AI-based Teacher Performance Evaluation System developed in Saudi Arabia leverages algorithmic weighting of key performance indicators (KPIs) to produce accurate, evidence-based teacher assessments (Discover Applied Sciences, 2024).

At the level of self-evaluation, the Teacher Artificial Intelligence Competence Self-Efficacy Scale (TAICS) provides a validated instrument to measure teachers’ readiness and confidence in using AI for pedagogical innovation (Education and Information Technologies, 2024). Collectively, these frameworks establish a foundation for AI-driven diagnostics of teaching effectiveness, while adaptive learning systems can deliver responsive professional development aligned with detected needs.

Theoretical Basis and Framework

AI-powered skill gap analysis in education operates through five interrelated phases: a) data collection, b) competency mapping, c) gap identification, d) adaptive learning, e) and iteration. Data are drawn from multiple sources, such as classroom observations, student evaluations, peer reviews, and LMS analytics, to feed machine learning models that compare current performance with established teaching standards (Hotwani, 2025).

In teacher development, this process can be visualized as a continuous loop:

This model aligns with the concept of adaptive learning ecosystems, which Hotwani (2025) describes as AI systems that “continuously evolve through feedback loops,” ensuring that professional learning remains relevant and effective.

Identifying Teaching Coaches through AI-Driven Evaluation

One promising application of AI-powered analysis lies in identifying teachers with high leadership or mentoring potential. Language schools can use performance data to locate educators who demonstrate exceptional communication, reflection, and peer collaboration. Machine learning models may analyze variables such as peer feedback sentiment, classroom engagement metrics, and student progress indicators to identify those whose teaching practices positively correlate with learner outcomes.

According to the OECD (2024), AI in education “can strengthen teacher agency and collaboration by supporting virtual coaching and peer mentoring systems” (p. 11). When a teacher exhibits strong performance consistency and improvement across multiple areas, AI-supported systems can recommend them for leadership tracks or teacher coaching programs. This automated identification process complements traditional human evaluation, reducing bias and accelerating leadership development pipelines within schools or institutions.

Case Studies and Applications

Empirical evidence supports the viability of AI-powered gap analysis for education. Almubarak, Alhalabi, Albidewi, and Alharbi (2025) demonstrated that automated image and video processing can classify classroom events (e.g., teacher questioning, student engagement) to provide real-time feedback. Similarly, in corporate learning contexts, Hotwani (2025) highlighted how AI identified deficiencies in negotiation skills within a global sales team, leading to a 22 percent increase in performance outcomes after targeted training. This principle can easily translate to education: AI could detect teachers’ recurring difficulties, such as diminished communication-oriented activities, limited digital integration overlooking the SAMR principles, classroom management challenges, or the amount of teacher talk as opposed to student talk, and then recommend microlearning modules accordingly.

Another example is the Saudi AI-based Teacher Performance Evaluation System (Discover Applied Sciences, 2024), which integrates human expertise with algorithmic scoring to ensure objectivity and reliability. This model could enable educational administrators to detect not only underperformance but also excellence, signaling candidates for coaching or supervisory roles within the organization or academic departments.

Ethical and Practical Considerations

Despite its promise, AI-based evaluation systems must address several ethical and practical issues. First, data privacy is paramount: classroom recordings, feedback, and performance data contain sensitive information requiring strict compliance with regulations stated by the institution aligned with the country’s laws. Second, algorithmic bias can distort results if training data are not representative of diverse teaching styles or teaching methodologies. Third, teacher trust and buy-in are essential; educators must perceive AI-powered evaluations as supportive rather than punitive. As Hotwani (2025) cautions, “human decision-making is not replaced by machines; rather, it is enhanced by it”. Institutions should thus pair AI analytics with human mentorship and qualitative reflection to ensure balanced, ethical decision-making.

Implications for Educational Leadership and Policy

Integrating AI-powered skill gap analysis into educational systems offers long-term benefits for leadership & coaching cultivation, institutional development, and the spotting of areas where teachers need to continue developing themselves in terms of classroom delivery. Administrators, on the other hand, can establish continuous professional learning cycles, supported by adaptive AI tools that align individual teacher growth with pedagogical principles, methodological aims, and organizational goals. Over time, these data insights can inform promotions, coaching assignments, and strategic hiring for key position within the institution. As the OECD (2024) emphasizes, when used responsibly, AI can “promote equity and inclusion in teacher development by tailoring professional learning opportunities to each educator’s context” (p. 15). Thus, AI not only modernizes evaluation but also democratizes access to growth and leadership pathways.

Conclusion

AI-powered skill gap analysis represents a paradigm shift and a new growth mindset in educational performance evaluation and leadership development. By synthesizing diverse data sources through machine learning, institutions can pinpoint skill deficiencies, personalize teacher training, and identify potential coaches or supervisors with unusual accuracy. However, this transformation must proceed with careful attention to privacy, transparency, and human oversight. Ultimately, AI should be viewed as an ally that enhances professional growth and organizational learning rather than a replacement for human discernment. As education in 2025 continues to evolve, institutions that embrace AI responsibly will be best equipped to cultivate resilient, reflective, and future-ready teaching teams.

📚 References

Almubarak, A., Alhalabi, W., Albidewi, I., & Alharbi, E. (2025). An AI-powered framework for assessing teacher performance in classroom interactions: A deep learning approach. Frontiers in Artificial Intelligence, 8(1553051).

Discover Applied Sciences. (2024). An analytical approach for an AI-based teacher performance evaluation system in Saudi Arabia’s schools. https://doi.org/10.1007/s42452-024-06117-4

Education and Information Technologies. (2024). Development and validation of the teacher artificial intelligence competence self-efficacy (TAICS) scale. https://doi.org/10.1007/s10639-024-13094-z

Hotwani, K. (2025). AI-powered skill gap analysis: Tailoring custom eLearning modules to individual needs in 2025. Custom eLearning Blog. Upside Learning.

Organisation for Economic Co-operation and Development. (2024). The potential impact of artificial intelligence on equity and inclusion in education. OECD Publishing. https://doi.org/10.1787/15df715b-en

Conceptual Model

Visualization - Conceptual Model [Handout] by Jonathan Acuña

AI-Powered Skill Gap Analysis and the Discovery of Teaching Coaches by Jonathan Acuña