Implications of Artificial Intelligence on Assessment Models in Nigeria

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Implications of Artificial Intelligence on Assessment Models in Nigeria

Implications of Artificial Intelligence on Assessment Models in Nigeria

Artificial Intelligence (AI) is an innovative technology that is transforming various sectors globally, including education.

According to a recent report, the global AI market is projected to grow from $93.5 billion in 2021 to $1,581.24 billion by 2030.

In Nigeria, integrating AI into assessment models holds significant implications as the education system continues to evolve.

Traditionally, assessments in Nigeria have relied on standardized tests and teacher evaluations, often criticized for their subjectivity and limited scope. AI-powered tools offer intriguing alternatives.

AI-driven assessment solutions utilize intelligent algorithms and machine learning to offer adaptive testing, personalized feedback, and automated scoring.

Some key examples include:

·       Computer adaptive tests (CATs) that customize test questions based on student responses. This allows for a more precise evaluation of skill levels.

·       Automated essay scoring engines that provide instant feedback on written answers using natural language processing.

·       Intelligent data analytics that generates actionable insights from assessment data.

These innovations could make assessment continuous, individualized, and focused on actual concept mastery.

Implications of Artificial Intelligence on Assessment Models in Nigeria
Enhancing Reliability and Removing Human Bias

By eliminating human subjectivity and fatigue effects during manual grading, AI promises more consistent and reliable test scoring. Automated assessments analyze inputs based solely on academic merit, devoid of any conscious or unconscious biases.

This ensures fairness and transparency while upholding accuracy standards. Carefully designed algorithms can also mitigate the high costs associated with manual scoring of large-scale, high-stakes exams.

However, these advancements come with a caveat:

·        Bias and discrimination: AI algorithms are limited by the quality of data they are trained on. Biases in datasets can perpetuate inequalities in educational outcomes, disproportionately impacting marginalized groups. Ensuring fair and ethical development of AI assessment tools is crucial.

·       Overreliance on technology: While AI can be a powerful tool, it should never replace the human element in education. The critical thinking, creativity, and social-emotional skills fostered by human interaction remain irreplaceable.

·       Digital divide: Unequal access to technology can exacerbate existing inequalities. Ensuring inclusivity by providing all students with necessary resources and digital literacy training is vital.

Nigeria’s journey towards integrating AI into its education system requires careful consideration of these implications. A multi-stakeholder approach involving educators, policymakers, and AI developers is necessary to:

·       Develop robust ethical frameworks: Establishing clear guidelines for the development and use of AI in assessments is crucial to mitigate bias and promote fairness.

·       Invest in teacher training: Equipping teachers with the skills to navigate the AI landscape and use these tools effectively is essential.

·       Prioritize digital inclusion: Bridging the digital divide through infrastructure development and targeted interventions is crucial to ensure equitable access to AI-powered learning opportunities.

Implications of Artificial Intelligence on Assessment Models in Nigeria
The Future of Assessment in Nigeria

While AI assessment tools are still emerging in Nigeria, global trends highlight the massive potential of these technologies. As Nigeria moves towards its Education 5.0 goals, incorporating AI to improve evaluations and unlock the value of assessment data will be crucial.

However, successful integration requires updating policies, building technical capacity, increasing digital literacy, and addressing ethical risks relating to student privacy and system biases.


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