Design of an AI-Based Performance Evaluation System for Secondary School Teachers in Minna Local Government, Niger State
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- NGN 5000
Background of the Study
Teacher performance evaluation is a critical component of educational quality assurance, particularly in secondary schools where instructional effectiveness directly impacts student learning outcomes. In Minna Local Government, Niger State, traditional performance evaluation methods for secondary school teachers often involve periodic observations and subjective assessments, which can be inconsistent and prone to bias. The advent of artificial intelligence (AI) offers the potential to transform these evaluations by providing a systematic, data-driven approach. An AI-based performance evaluation system can utilize machine learning algorithms to analyze multiple data sources, including classroom observations, student feedback, and performance metrics, to generate objective evaluations of teacher effectiveness (Olu, 2023). Such systems can continuously monitor teaching practices, identify trends, and offer real-time feedback that supports professional development. By leveraging big data and advanced analytics, AI-based evaluations can ensure that performance assessments are consistent, fair, and aligned with educational standards (Adebayo, 2024). This innovative approach not only reduces the administrative burden on evaluators but also enables a more dynamic and responsive evaluation process that can adapt to changing educational contexts. Despite these potential benefits, the implementation of AI-based systems faces challenges such as data privacy, the need for high-quality training data, and the potential for algorithmic bias. Stakeholders also express concerns about the dehumanization of the evaluation process and the loss of nuanced judgment that experienced evaluators provide. This study seeks to design an AI-based performance evaluation system tailored for secondary school teachers in Minna Local Government, comparing its outcomes with traditional methods and providing recommendations to overcome technical and ethical challenges (Balogun, 2025).
Statement of the Problem
Secondary schools in Minna Local Government currently employ traditional teacher performance evaluation methods that are largely subjective and infrequent, leading to inconsistent feedback and limited opportunities for professional improvement (Olu, 2023). These conventional methods rely on periodic classroom observations and written evaluations, which are susceptible to personal biases and may not capture the full spectrum of a teacher’s performance. Although an AI-based system promises more objective and continuous evaluation by analyzing quantitative data and providing real-time insights, its implementation is not straightforward. Challenges such as ensuring the accuracy of data inputs, addressing concerns over data privacy, and mitigating algorithmic bias remain significant barriers (Adebayo, 2024). Additionally, there is resistance among some educators who are wary of automated evaluations that may not fully consider the qualitative aspects of teaching. The lack of a comprehensive, technology-driven evaluation framework limits the ability of schools to implement timely interventions that could enhance teacher performance and student outcomes. This study seeks to bridge the gap between traditional evaluation practices and advanced AI methodologies by developing and testing an AI-based performance evaluation system, thereby providing a more reliable and actionable basis for teacher development (Balogun, 2025).
Objectives of the Study:
• To design an AI-based performance evaluation system for secondary school teachers.
• To compare the system’s performance with traditional evaluation methods.
• To recommend strategies for addressing data quality and privacy concerns.
Research Questions:
• How effective is the AI-based evaluation system in assessing teacher performance?
• What challenges exist in integrating AI into performance evaluations?
• How can data privacy and algorithmic bias be mitigated?
Significance of the Study
This study is significant as it explores the design and implementation of an AI-based performance evaluation system for secondary school teachers, promising enhanced objectivity, consistency, and timely feedback. The findings will provide actionable recommendations for improving teacher assessments and professional development in Minna Local Government, ultimately contributing to better educational outcomes (Olu, 2023).
Scope and Limitations of the Study:
This study is limited to secondary school teacher performance evaluation in Minna Local Government, Niger State.
Definitions of Terms:
• AI-Based Evaluation System: A system that uses artificial intelligence to assess performance based on quantitative data (Adebayo, 2024).
• Teacher Performance Evaluation: The process of assessing the effectiveness of teaching (Olu, 2023).
• Algorithmic Bias: The tendency of AI systems to produce prejudiced outcomes due to biased data (Balogun, 2025).
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