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learning outcomes, while also supporting self-regulated learning and motivation (Hattie &
Timperley, 2007; Sadler, 1989). When feedback is timely, specific, and constructive, it can
improve both performance and confidence (Nicol & Macfarlane-Dick, 2006). At the same
time, it enables educators to refine their teaching by addressing misconceptions and
adapting to learners’ needs (Shute, 2008). Overall, formative feedback transforms
assessment into a continuous process that supports learning rather than merely
evaluating it (Black & Wiliam, 1998).
However, traditional feedback approaches often suffer from delays and
inconsistency, which can negatively affect student engagement and learning outcomes
(Luckin et al., 2016). In response, AI-assisted feedback tools have emerged as a scalable
solution, offering timely and personalised responses that support both understanding and
independent learning (Zawacki-Richter et al., 2019; Kasneci et al., 2023). These systems
can generate real-time insights and assist educators in managing large volumes of student
work more efficiently (Analytikus, 2024; Guo, 2024).
Despite these advancements, there is still limited empirical evidence on how AI--
assisted feedback can be effectively implemented and accepted within real educational
settings, particularly in Open and Distance Learning (ODL) environments. This highlights a
critical research gap in understanding both its practical integration and its impact on
learners.
3. Objectives
The objectives of this paper are listed below.
To describe the implementation of an AI-assisted Feedback System at Open
University Malaysia (OUM) to support formative assessment through assignments; and
To examine students’ satisfaction with the system, their willingness to continue
using it for improving assignments, and their perceptions of the negative feedback
generated by the system.
This study is important as it contributes to the broader discussion on improving
feedback practices within AI-assisted educational environments.
4. Methodology
This study adopts a quantitative approach to evaluate the effectiveness of an AI-
assisted Feedback System (AIFS) and examine users’ perceptions of its role in formative
assessment. The system was piloted across 12 first-semester courses spanning various
disciplines and learner groups. The participants were students enrolled in these courses
during a 14-week semester. They were encouraged to submit multiple drafts to the AIFS
platform, allowing them to receive iterative feedback in the form of inline comments and
summary reports. This process supported continuous revision and improvement while
promoting independent learning, as the system guided students without directly
modifying their work.
The survey measured three key aspects—student satisfaction, intention to reuse
the system, and responses to negative feedback—each assessed on a five-point Likert
scale (1 = strongly disagree to 5 = strongly agree). The survey items are given in Table 1.
At the end of the semester, students were invited to complete the online survey.
The data were analysed using descriptive statistics, particularly mean values, to
assess levels of satisfaction, intention, and feedback perception. The results were
interpreted to evaluate students’ acceptance of AI--assisted feedback and its potential to
support formative assessment in ODL contexts. Figure 1 presents an overview of the
study’s methodology and research workflow.

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