NZQA needs more alternatives to the current exam, so that they can deliver on differing days, as their current online exam system is not robust enough for the relatively small number of students currently using the software. It seems strange that government will go out and look for new (or somethings bespoke) software solutions, when there are already robust options available (e.g. Moodle Learning Management System) - but this inability to understand the online learning ecosystem is a topic for another post.
Regardless, NZQA thinks that by having more alternatives to the exam they write - created by AI and then reviewed by humans - they can offer the exam on various days, as a workaround for their IT issues.1 This is an incredibly limited approach to the use of AI. With this technology, exams can be created for each student that tests their knowledge individually. Using AI simply to do the work of a few extra staff that they don’t want to hire is a shortsighted approach.
Let’s consider the idea of using AI to create exams more broadly. Some of these comments apply to formative assessments more than summative, but should give an idea of ways that AI can be used, beyond simply reworking a human created exam.
In the realm of education, personalised learning has gained significant traction as a means to cater to the individual needs and abilities of students. One powerful application of Artificial Intelligence (AI) in this domain is the creation of personalised exams. By leveraging AI algorithms and techniques, educators can tailor assessment materials to align with the unique learning profiles of each student, thereby enhancing engagement, understanding, and overall academic performance.
Before delving into the creation of personalised exams, it’s crucial to comprehend the diverse profiles of students. AI can assist in this aspect by analysing various data points, including academic records, learning styles, cognitive abilities, and preferences. Through Machine Learning (ML) algorithms, educators can gain insights into each student’s strengths, weaknesses, areas of interest, and levels of proficiency across different subjects and topics.
One of the key features of personalised exams is adaptive testing. AI-powered systems can dynamically adjust the difficulty level of questions based on the student’s responses in real-time. Adaptive testing algorithms use item response theory (IRT) and Bayesian inference to continually assess the student’s performance and adapt the exam accordingly. This ensures that each student is challenged at an appropriate level, fostering a supportive learning environment where they can progress at their own pace.
AI enables the customisation of exam content to suit the specific needs and preferences of individual students. Natural Language Processing (NLP) algorithms can analyse textbooks, lecture notes, and other educational resources to generate questions that are relevant to the student’s learning objectives and curriculum requirements. Additionally, Machine Learning (ML) models can identify and prioritise topics that require more attention based on the student’s performance history, allowing educators to focus on areas that need reinforcement.
In addition to generating exam questions, AI can provide personalised feedback and remediation strategies to help students improve their understanding and performance. Through data analytics and predictive modelling, AI systems can pinpoint misconceptions and areas of misunderstanding in real-time. Based on this analysis, tailored feedback can be delivered to students, along with targeted resources and learning materials to address their specific learning needs and reinforce conceptual understanding.
Another benefit of using AI to create personalised exams is the enhancement of accessibility and inclusivity for all students, including those with disabilities or special educational needs. AI-powered exam platforms can offer adaptive features such as text-to-speech, speech recognition, and alternate input methods to accommodate diverse learning preferences and abilities. By removing barriers to access, educators can ensure that every student has an equal opportunity to demonstrate their knowledge and skills.
While AI offers tremendous potential in personalised exam creation, it’s essential to address ethical considerations and data privacy concerns. Educators must ensure transparency and fairness in the use of AI algorithms, avoiding bias or discrimination in exam content or assessment processes. Moreover, strict protocols must be implemented to safeguard student data and privacy rights, adhering to established regulations.
The use of AI in personalised exam creation is an iterative process that requires continuous improvement and refinement over time. Educators and AI developers should collaborate closely to gather feedback from students and instructors, iteratively enhancing the accuracy, effectiveness, and user experience of AI-powered exam platforms. By embracing a culture of continuous improvement, educational institutions can harness the full potential of AI to facilitate personalised learning and assessment practices.
AI offers unprecedented opportunities to create personalised exams that cater to the individual needs and abilities of students. By leveraging adaptive testing mechanisms, content customisation algorithms, and personalised feedback strategies, educators can enhance engagement, understanding, and academic performance. Moreover, AI enables the enhancement of accessibility and inclusivity, ensuring that all students have equal opportunities to succeed. However, ethical considerations and data privacy concerns must be carefully addressed to uphold fairness and transparency in the use of AI in education. With thoughtful implementation and iterative development, AI-powered personalised exams have the potential to revolutionise the assessment landscape, empowering students to achieve their full potential.
1 https://www.rnz.co.nz/news/national/515688/could-ai-be-used-to-help-write-school-exams-nzqa-thinks-so, Accessed 02 May 2024
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