The challenges facing traditional feedback mechanisms, especially in large-scale Open and Distance Learning (ODL) environments like OUM, include delays in delivery, inconsistencies in quality, and scalability issues, all of which can hinder student engagement and learning outcomes. By partnering with Studiosity, OUM sought a solution to provide timely, consistent, and personalised formative feedback to its diverse student population.
With a population of 37,000, the majority (95%) of OUM's students are working adults, mainly aged between 29-45 years old. OUM was seeking a way to scale its support service while delivering the personalisation and modern technology that students expect.
Vice President and Deputy Vice Chancellor Prof Dr Santhi Raghavan sought the opportunity to integrate AI into the teaching and learning processes at OUM, without compromising degree integrity and while maintaining and hopefully boosting OUM's already high retention rate of 88%.
"I look at this as; it has integrity, it is ethical, and it is there to support the students in their learning experience."
- Prof Dr Santhi Raghavan, OUM
To measure the students' impressions of AI-powered feedback, a quantitative research design was employed at OUM, surveying 435 students who used Studiosity during the January 2025 semester. The study focused on four key constructs: Intention to Use (Students' willingness to continue using the AI-based system), Satisfaction (Students' contentment with the system's performance and usefulness), Trust (Students' confidence in the AI-based system and its feedback), and Perception of Negative Feedback (Students' emotional and cognitive responses to critical feedback from the AI).
Data was collected via an online survey using a five-point Likert scale. A subset of 211 students who received critical feedback completed a specific section on their experiences.
Descriptive statistics revealed high levels of student intention to continue using the system (M=4.16), satisfaction (M=4.20), and trust (M=4.03). This indicates strong acceptance and positive experiences with Studiosity.
Emotional responses to negative feedback were more variable (M=3.45-3.80). While students generally recognised the cognitive value of negative feedback (e.g., identifying weaknesses, using it for revision), some reported feeling demotivated. This highlights the need for emotionally sensitive feedback mechanisms.
The data showed no statistically significant differences in perceptions based on gender or level of study (undergraduate vs. postgraduate). This indicates Studiosity's general acceptability and perceived value across diverse learner demographics, reinforcing its potential for inclusive digital learning environments.
The study underscores the significant value of AI-assisted feedback in promoting self-directed learning and improving academic writing, particularly in ODL contexts.
The implementation of Studiosity at OUM shows the strong potential of AI in enhancing formative assessment. Students highly value its usefulness and are satisfied with its performance. With an embedded, whole-of-institution approach, these results could easily be seen at scale with OUM's 37,000 distance learners.
This system does not directly edit or change the content of student scripts. Instead, it highlights and discusses commonly made mistakes, incorporating examples to help students understand and address these issues. This approach empowers students to apply the feedback to their current and future writing tasks, fostering long-term critical-thinking skill development.
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Dr Nantha Kumar Subramaniam, co-author of the research paper, presenting his findings at the
'AI for Learning Forum' in Malaysia in April.
Participants in the AI for Learning Forum, hosted by OUM.
