As generative artificial intelligence (Gen AI) tools become increasingly integrated into language education, understanding how learners respond to AI-generated feedback during writing revision has become an important research concern. Existing studies have primarily examined the effects of AI on writing performance, with less attention to learners' reported decision-making and reflection during revision. This qualitative study investigated learner-AI interaction, reported revision practices and learner perceptions among 18 first-year non-English-major students enrolled in a university EFL writing course. Data were collected through semi-structured interviews following a Gen AI-assisted revision task and analysed using thematic analysis. The findings identified three interaction patterns with Gen AI: passive AI reliance, adaptive AI engagement, metacognitive AI engagement. Participants differed in how they evaluated and used AI-generated feedback, including passive acceptance, selective uptake, and critical evaluation. Some reported increased confidence, motivation, and awareness of linguistic form, whereas others expressed concerns about overreliance, authorship, and loss of personal voice. These self-reported experiences provide insight into how EFL learners perceive and manage GenAI-assisted revision, highlighting the need for pedagogical approaches that foster critical AI literacy and reflective learner autonomy.
Accurately estimating population sizes for free-ranging animals through noninvasive methods, such as camera trap images, remains particularly limited by small datasets. To overcome this, we developed a flexible model for estimating upper limit populations and exemplified it by studying a group-living synanthrope, the long-tailed macaque (Macaca fascicularis). Habitat preference maps, based on environmental and GPS data, were generated with a maximum entropy model and combined with data obtained from camera traps, line transect distance sampling, and direct sightings to produce an expected number of individuals. The mapping between habitat preference and number of individuals was optimized through a tunable parameter ρ (inquisitiveness) that accounts for repeated observations of individuals. Benchmarking against published data highlights the high accuracy of the model. Overall, this approach combines citizen science with scientific observations and reveals the long-tailed macaque populations to be (up to 80%) smaller than expected. The model's flexibility makes it suitable for many species, providing a scalable, noninvasive tool for wildlife conservation.