Self-assessment is believed to complement peer assessment in the classroom. However, whether, how and why this is done remains unclear. This study, by investigating the combined use of self and peer assessment for an academic writing task among a group of undergraduate students in Hong Kong, aims to shed light on how self-assessment complements peer assessment. Self-assessment is found to complement peer assessment in five ways: 1) it guides students to revise when peer assessment is lacking; 2) self-assessment effectively supplements peer assessment when the students had access to peer assessment; 3) even if a student has access to quality peer assessment, self-assessment complements peer assessment due to the different reflection involved in the two processes; 4) self-assessment can supplement peer assessment in terms of issues related to social-affective burdens for the latter; 5) self-assessment also complements peer assessment in that it benefits high and low-achieving students. Two problems surfaced. One is the students’ antipathy against self-assessment despite their overall positive perception about peer assessment; the other is the inadequacy of combining self with peer assessment in fostering learning outcomes regardless that the combined use of self and peer assessment helped the students with revision considerably.

Recent years have witnessed a growing application of generative artificial intelligence (GenAI) technology in writing instruction. Students should mobilise their metacognitive strategies during this endeavour to maximise the benefits of GenAI while avoiding the potential negative impacts. Within the context of tertiary education in Hong Kong, this qualitative research investigates 13 postgraduate students' metacognitive strategies when utilising ChatGPT for English academic writing. Using data collected from audio recordings, semi‐structured interviews and human–GenAI chat logs, we explored students' metacognitive strategies within five dimensions, namely, planning, monitoring, evaluating, information management and debugging. The identified themes and subthemes within each dimension exhibited similarities to those observed in other educational settings. Meanwhile, certain metacognitive strategies (e.g., debugging) were found to be unique to the context of GenAI‐supported academic writing. This study contributes to the literature in two ways. Firstly, it provides a detailed and thorough understanding of metacognitive strategies, complementing the extensive quantitative research in this area. Secondly, this research expands the framework of metacognitive strategies to the context of GenAI‐supported academic writing learning.