Search

Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners' help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI. • The help-seeking process of learners seeking help from AI is non-linear. • Learners' help-seeking from human experts is more linear and aligns with theory. • Learners seeking help from ChatGPT showing pragmatic help-seeking activities. • Learners seeking help from human experts were more proactive in metacognition. • Scaffolding is needed to better facilitate learning with AI. [ABSTRACT FROM AUTHOR]

Lovastatin, a secondary metabolite isolated from fungi, is often used as a representative drug to reduce blood lipid concentration and treat hypercholesterolemia. Its structure is similar to that of HMG-CoA. Lovastatin inhibits the binding of the substrate to HMG-CoA reductase, and strongly competes with HMG-CoA reductase (HMGR), thereby exerting a hypolipidemic effect. Further, its safety has been confirmed in vivo and in vitro. Lovastatin also has anti-inflammatory, anti-cancer, and neuroprotective effects. Therefore, the biological activity of lovastatin, especially its anti-cancer effect, has garnered research attention. Several in vitro studies have confirmed that lovastatin has a significant inhibitory effect on cancer cell viability in a variety of cancers (such as breast, liver, cervical, lung, and colon cancer). At the same time, lovastatin can also increase the sensitivity of some types of cancer cells to chemotherapeutic drugs and strengthen their therapeutic effect. Lovastatin inhibits cell proliferation and regulates cancer cell signaling pathways, thereby inducing apoptosis and cell cycle arrest. This article reviews the structure, biosynthetic pathways, and applications of lovastatin, focusing on the anti-cancer effects and mechanisms of action. • Lovastatin can regulate the proliferation, apoptosis and drug resistance in different types of cancer cells in breast, liver, lung, colon, gastric, etc. • In addition to being used to treat hypercholesterolemia, lovastatin also exerts anti-inflammatory, neuroprotective, depressive symptoms and anti-cancer effects. • Lovastatin inhibits cell proliferation and regulates cancer cell signalling pathways, thereby inducing apoptosis. [ABSTRACT FROM AUTHOR]