This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.

Results of recent studies have suggested that intensive methods of delivery might improve engagement, attendance, and achievement for students from diverse backgrounds. Contributing to this area of inquiry, this study assesses how students perceived their experience studying a certificate course that was delivered in an online intensive block mode and flipped classroom (BMFC), pedagogy amidst COVID-19 restrictions. The subjects were students enrolled at Melbourne Institute of Technology between July 2021 and January 2022 across four certificate courses, three at postgraduate and one at undergraduate level. These certificate courses differed from normal degree courses in several aspects: (a) a shorter 4-week (undergraduate) or 5-week (postgraduate), instead of a 12-week duration, (b) subjects were taken sequentially instead of concurrently as in a normal semester, (c) taught using an online flipped classroom rather than the in-class approach, and (d) open to both high-school leavers and mature aged students who did not study full-time. A questionnaire involving 10 perception-based questions was used to survey students’ satisfaction with the BMFC delivery, in relation to their learning and engagement experience. The mean, median, and mode calculated from the responses revealed that students regarded the BMFC approach as more satisfied than not on a 5-star rating scale in 7 out of the 10 questions. This is further supported by high correlations among the questions (the lowest at r = 0.48 and the highest at r = 0.87). Multiple regression analysis using the first nine questions as predictors of the 10th question (overall satisfaction) revealed that six of these are statistically significant predictors (p < 0.05) of the overall satisfaction, implying that an increase in the overall satisfaction can potentially be achieved by improving these key factors of the BMFC delivered certificate courses. Our findings correlate with existing research that student learning and engagement might be improved by intensive modes of delivery. Furthermore, the BMFC pedagogy proposed in our study differentiates us from existing research, where block scheduling was used only in a face-to-face delivery in pre COVID-19 environment. Our study, therefore, contributes a novel delivery method for learning and teaching that is suitable for both online and face-to-face mode in a post COVID-19 era.

Websites employ third-party ads and tracking services leveraging cookies and JavaScript code, to deliver ads and track users’ behavior, causing privacy concerns. To limit online tracking and block advertisements, several ad-blocking (black) lists have been curated consisting of URLs and domains of well-known ads and tracking services. Using Internet Archive’s Wayback Machine in this paper, we collect a retrospective view of the Web to analyze the evolution of ads and tracking services and evaluate the effectiveness of ad-blocking blacklists. We propose metrics to capture the efficacy of ad-blocking blacklists to investigate whether these blacklists have been reactive or proactive in tackling the online ad and tracking services. We introduce a stability metric to measure the temporal changes in ads and tracking domains blocked by ad-blocking blacklists and a diversity metric to measure the ratio of new ads and tracking domains detected. We observe that ads and tracking domains in websites change over time, and among the ad-blocking blacklists that we investigated, our analysis reveals that some blacklists were more informed with the existence of ads and tracking domains, but their rate of change was slower than other blacklists. Our analysis also shows that Alexa top 5K websites in the US, Canada, and the UK have the most number of ads and tracking domains per website, and have the highest proactive scores. This suggests that ad-blocking blacklists are updated by prioritizing ads and tracking domains reported in the popular websites from these countries.