The Shift in Assessment: Why the TVET Sector Can’t Afford to Wait
AI is transforming education—but unless the TVET sector accelerates adoption, it risks falling behind in preparing learners for an increasingly AI-driven workforce.
As AI becomes more common in schools, it's important to understand its ethical implications. AI tools, such as those powered by Large Language Models (LLMs), use data to create content. The data used to train these models significantly influences the output they generate. For instance, if the training data predominantly includes materials from Western educational systems, focuses on urban-centric issues, or comes from a particular socioeconomic group, the resulting content will reflect these biases. Consequently, the inclusivity and equity of LLMs depend heavily on the diversity of the information on which they are trained. This scenario highlights the importance of examining the ethical dimensions of AI in education, particularly regarding its potential to reinforce biases, impact student and teacher autonomy, and affect privacy and data security.
Educators must proactively address these ethical concerns to ensure that AI tools enhance rather than hinder the educational experience. This blog highlights the key ethical issues that arise with the integration of AI in education, along with suggestions on how to address them.
What can educators do?
Ask questions related to the materials used to train the AI models. Do they reflect a wide range of perspectives and backgrounds?
Conduct regular training for educators on identifying and addressing bias in AI tools and teaching materials.
Check if there is a feedback loop where students & teachers can report instances of perceived bias, allowing for continuous improvement.
What can educators do?
Develop and communicate clear data privacy policies to students and parents, outlining what data is collected and how it is used.
Implement strict access controls to ensure that only authorised personnel can access sensitive student data.
Conduct regular audits of data security practices to identify and address potential vulnerabilities.
What can educators do?
Use AI tools to supplement rather than replace teacher-led instruction, allowing teachers to guide and oversee AI use.
Allow students to choose how they interact with AI tools, fostering a sense of control and independence.
Provide professional development on integrating AI tools in a way that supports student & teacher autonomy.
What can educators do?
Use AI tools that offer clear explanations for their recommendations, helping teachers and students understand AI decisions.
Maintain transparency about how AI tools are used and evaluated in the classroom.
Establish a clear framework that outlines the roles and responsibilities of educators, administrators, and developers in AI implementation.
What can educators do?
Integrate AI tools into a blended learning approach that combines digital and face-to-face interactions.
Evaluate AI tools not just on academic outcomes but also on their impact on student engagement and social development.
Adjust the use of AI tools based on classroom dynamics and individual student needs.
What can educators do?
Develop and adhere to ethical guidelines for AI use in the classroom, ensuring AI tools are used responsibly.
Involve parents, students, and community members in discussions about the ethical use of AI.
Conduct regular reviews of AI tools to ensure they align with educational values and goals.
Ultimately, the goal is to create a balanced approach that leverages the benefits of AI while safeguarding against its potential drawbacks. By prioritising fairness, transparency, and responsible use, educators can harness the potential of AI to foster an inclusive, secure, and engaging learning environment for all students.
For further reading:
https://ictevangelist.com/the-eu-ai-act-and-ten-things-it-means-for-uk-schools/
https://teaching.cornell.edu/generative-artificial-intelligence/ethical-ai-teaching-and-learning
AI is transforming education—but unless the TVET sector accelerates adoption, it risks falling behind in preparing learners for an increasingly AI-driven workforce.
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