The instructional effectiveness of artificial intelligence (AI) dialogue tools in higher education varies across contexts and depends on multiple moderation mechanisms. This structured evidence synthesis of eight studies identifies four moderation dimensions: task embedding depth, feedback granularity, teacher orchestration intensity, and discipline verification pressure. These dimensions are examined through task cycles, feedback characteristics, teacher intervention, and verification requirements. The findings show that higher intensity configurations with structured task embedding, layered feedback, teacher orchestration, and content verification are associated with higher composite coding indices across learning gains, engagement, trust, and knowledge transfer. A four-stage closed-loop learning pathway is proposed to support the pedagogical integration of AI dialogue tools. The findings highlight the importance of moderation mechanisms, teacher orchestration, and feedback granularity in designing effective AI-supported learning environments.
Traditional English translation teaching faces three core challenges: incomplete learning records, unreliable peer assessment, and difficulty measuring contributions to student revision. This paper proposes a blockchain-based process evaluation framework with multi-agent certification, event coding, and early-warning loops. A semester-long experiment involving 96 students, 18 translation tasks, and six-dimensional evaluation indicators retained 88–96% complete learning records with diverse interference. As teaching proceeds, timely feedback rates rise from 84% to 92%, average student revisions increase from 1.4 to 2.4, while peer evaluation conflicts drop from 6.8% to 4.1%. Feedback delays weaken translation improvement, and the campus network and unified scoring criteria limit system performance. The blockchain transforms scattered learning data into traceable evidence, optimizes multi-party collaborative assessment, and offers a viable technical path for reforming English translation teaching.
In view of the current situation that correcting errors manually is difficult, and the teaching adaptability of intelligent devices is insufficient in physical education class, the author of this paper built a web-enabled computer vision-aided teaching system. The study constructed a closed-loop linkage between visual recognition and classroom teaching; set quantitative thresholds, such as recognition delay and key points, in different teaching scenarios; and clarified the rules of recognition fault tolerance and human-machine review for different training tasks. He designed the integrated implementation path of pre-class, in-class, after-class and semester evaluation. The results showed that the mixed teaching platform of human motion recognition could analyze students' movements in real time, push hierarchical error correction guidance, and reduce teachers' classroom management burden. The research provides a complete and feasible scheme for the landing of an intelligent sports network teaching platform in colleges and universities.
Blended teaching is a driver of Chinese classroom reform, yet its traditional disjointed teaching‒learning‒evaluation model has significant flaws, such as delayed feedback and data silos that could be ameliorated by simple technology. This study designs a multi-agent task‒execution‒feedback‒redistribution closed-loop mechanism and a teaching efficiency-based evaluation system. Via three-class experiments and multi-source data analysis, it verifies the mechanism's efficacy and explores how feedback/evaluation frequency affects teaching efficiency and students' multi-dimensional competency. The mechanism boosted task completion and competency scores of students in the experimental classes, optimized feedback chains, and fostered creativity and critical thinking, but caused higher group heterogeneity and individual feedback anxiety. It has self-repair ability and robustness against platform delays and external interference, taking support for disadvantaged groups and regulation of high-frequency feedback as its core optimization priorities. This study validates the innovation's value, offering references for technology‒classroom integration.
Against the backdrop of digital transformation, English teaching is constrained by over-reliance on single-modal resources and insufficient integration of web technologies and pedagogy, which hinder the development of learners' higher-order skills. Using mixed-methods research, this study constructs a cognition-centered multimodal knowledge management framework based on cognitive load theory. It develops a system for resource classification, scenario adaptation, and cognitive activation alongside a three-dimensional tagging mechanism and an augmented reality‒supported technology-teaching double-helix model. Proper visual‒auditory input matching facilitates learners' information retention. Empirical evaluation reveals positive changes in classroom group interaction, digital tool adoption, and students' written logical performance. This study offers practical optimization strategies and suggests bridging the digital divide, strengthening teachers' technological pedagogical content knowledge capability, and exploring artificial intelligence applications for web-based multimodal English teaching.
