In the last few months, the integration of Artificial Intelligence (AI) into subject knowledge assessments has gained significant traction, promising to enhance the whole assessment journey for students and assessors, improving overall efficiency and accuracy in evaluating student performance. As a provider of AI-assisted marking technology, we understand that the adoption of AI can spark a range of emotions, including some misconceptions and concerns. So I want to take this opportunity to offer a transparent view of how AI-driven assessments are reshaping assessments, using sAInaptic as an example.
One of the most prevalent fears is that AI when used for marking and providing feedback, might lead to the replacement of teachers. This concern arises from the fact that AI-assisted marking removes the teacher from this process, giving the perception that a teacher’s role is redundant.
However, this thinking overlooks the primary role of AI as a supportive tool rather than a substitute for human assessors. Marking short answer assessments and essays can be a tedious and repetitive task, especially when there are a large number of scripts to be marked in one go. And guess who is best at handling repetitive tasks? Artificial Intelligence!
So how about we help assessors with doing the first round of marking using an AI tool like sAInaptic, so they only have to review the marking and feedback given by the AI, giving them back valuable time and allowing them to focus on what they do best - engaging with students, providing targeted intervention, and fostering a hands-on learning environment. This way assessors are still fully in control of the marking and measurement process and their students receive moderated feedback.
Whether it’s formative assessments or feedback-driven learning, accuracy of marking and quality of feedback are a crucial factor in assessments, and some may question the reliability of AI-driven marking.
Well, first and foremost, at sAInaptic, we use your questions and marking schemes to train the AI to mark just as you would! It’s a specially trained model, not a general purpose one. We also continuously refine our AI models to ensure high levels of accuracy. In-app feedback loops allow both students and assessors to flag marking that is not reflective of the marking scheme and through rigorous human-in-the-loop moderation processes, we strive to provide marking that reflects true student performance. Moreover, human marking is also not accurate enough - reliability in marking between humans is measured as inter-rater variability for a reason!
Concerns about bias in AI have arisen primarily due to the popularity of generative AI tools that have been trained on data from the internet and other digital sources. Such data are only available in developed nations, where these models were developed and so reflect the people and culture of those regions. AI tools developed specifically for a narrow set of tasks such as diagnosing particular types of diseases, predicting trading outcomes or in our case, marking and feedback, are often built using custom data, sourced ethically from a diverse range of data sets and are therefore free of bias.
They are more reliable compared to humans and provide consistent, high quality outputs. It is crucial to recognise that AI systems are only as unbiased as the data they are trained on. At our company, we regularly audit our algorithm for fairness and accuracy.
Some educators and awarding organisations worry that integrating AI into their existing assessment for marking and feedback is not allowed by Ofqual. However, that’s not true. Ofqual clearly states that AI marking cannot be used as the sole marker in a high-stakes setting. They have been clear in communicating their pro-innovation approach to adopting AI in the qualifications sector and also have ensured fairness for students, validity of qualifications, security and maintaining public confidence as their top priorities.
The DfE recently announcing a share of £1M for technology companies in the sector to develop generative AI tools, specifically for marking to facilitate a reduction in workload is a clear signal that this is the way to go and sAInaptic is leading the way already.
This blog explores how AI marking is set to transform education by providing instant feedback, reducing teacher workload, and shifting assessments from high-stakes exams to continuous, growth-focused evaluations.
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This blog demystifies common misconceptions about AI in assessments, highlighting how AI supports teachers by improving marking efficiency and consistency, while addressing concerns about bias, accuracy, and compliance with regulations.
This blog demystifies common misconceptions about AI in assessments, explaining how AI supports rather than replaces teachers, ensures accuracy and fairness, and complies with regulations like Ofqual, enhancing both the efficiency and quality of the assessment process.