As the number of participants in online distance learning courses increases, peer assessment is becoming a popular strategy for evaluating open assignments and for breaking the social isolation surrounding distance education. Yet, the quality and characteristics of peer assessment in massive online courses has received little attention. Hence, this study was set to examine peer feedback quality and grading accuracy in a project-based course. The study applied a sequential exploratory mixed methods design. It included 339 participants who studied the same engineering course, but in three different modes: on-campus (n = 77), small private online course (n = 110), and massive open online course (MOOC) (n = 152). Content analysis of feedback comments identified four categories: reinforcement, statement, verification and elaboration, arranged in an ascending scale of cognitive ability. The findings indicated that the MOOC participants provided more feedback comments and volunteered to assess more projects than their counterparts did. However, the on-campus students provided higher quality feedback and their peer grading was better correlated with the grades assigned by the teaching assistants.

Background: As artificial intelligence (AI) technology rapidly advances, it becomes imperative to equip students with tools to navigate through the many intricate ethical considerations surrounding its development and use. Despite growing recognition of this necessity, the integration of AI ethics into higher education curricula remains limited. This paucity highlights an urgent need for comprehensive ethics education initiatives in AI, particularly for science and engineering students who are at the forefront of these innovations. Hence, this research investigates the role of an online explicit-reflective learning module in fostering science and engineering graduate students' ethical knowledge, awareness, and problem-solving skills. The study’s participants included 90 graduate students specializing in diverse science and engineering research tracks. Employing the embedded mixed-methods approach, data were collected from pre- and post-intervention questionnaires with closed-ended and open-ended questions. Results: The study's results indicate that the online explicit-reflective learning module significantly enhanced students' knowledge of AI ethics. Initially, students exhibited a medium–high level of perceived ethical awareness, which saw a modest but statistically significant enhancement following the participation. Notably, a more distinct increase was observed in students' actual awareness of ethical issues in AI, before and after the intervention. Content analysis of students’ responses to the open-ended questions revealed an increase in their ability to identify and articulate concerns relating to privacy breaches, the utilization of flawed datasets, and issues of biased social representation. Moreover, while students initially displayed limited problem-solving abilities in AI ethics, a considerable enhancement in these competencies was evident post-intervention. Conclusions: The study results highlight the important role of explicit-reflective learning in preparing future professionals in science and engineering with the skills necessary for ethical decision-making. The study highlights the need for placing more emphasis not only on students’ ability to identify AI-related ethical issues but also on their capacity to resolve and perhaps mitigate the impact of such ethical dilemmas.

This paper presents two complementary quantitative studies examining the integration of generative AI (GenAI) tools into programming courses in higher education. Study 1 investigated undergraduate students’ perceptions of GenAI tools, focusing on usefulness in coursework, creativity enhancement, behavioral intention to use, and concerns and critiques. Study 2 evaluated undergraduate students’ performance in correcting code generated by large language models (LLMs) during programming exams, comparing this performance to their results on instructor-designed programming tasks. Findings from Study 1 revealed generally favorable student perceptions of GenAI. Participants reported medium-to-high levels of perceived usefulness, particularly emphasizing GenAI’s potential to enhance learning efficiency and support creative problem-solving. Students also expressed strong intentions to continue using GenAI tools in their studies, while reported concerns were relatively low and centered primarily on the risk of over-reliance. Findings from Study 2 showed that students encountered significantly greater difficulty when correcting LLM-generated code compared to traditional exam tasks, highlighting the unique challenges posed by AI-generated outputs in assessment contexts. Taken together, the results suggest that while students recognize the value of GenAI tools in supporting learning and creative exploration, programming education must also focus on developing students’ skills in critically evaluating and correcting AI-generated content to ensure effective and responsible integration.