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Environ."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The integration of Artificial Intelligence (AI) into higher education promises to personalize learning, yet its rapid proliferation has outpaced the rigorous empirical evidence needed to guide its use, especially in complex humanities disciplines. The study of foreign literature, with its dual challenges of linguistic and cultural barriers, presents a high-stakes context where AI support is theoretically valuable but empirically unverified. This study, therefore, evaluated the effectiveness of a bespoke AI Digital Teacher designed to mitigate these challenges. In a semester-long randomized controlled trial, eighty-four undergraduate literature students were assigned to either a control group (traditional instruction) or an experimental group (traditional instruction plus AI teacher access). Outcome measures included academic performance, achievement emotions, and cognitive load. The results demonstrated a profound impact. The AI group significantly outperformed the control group on both objective tests (\n                    <jats:italic>M<\/jats:italic>\n                    \u2009=\u200981.9 vs. 75.2,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u2009.003) and, most notably, on analytical essays (\n                    <jats:italic>M<\/jats:italic>\n                    \u2009=\u200983.5 vs. 71.3,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.001). Furthermore, a significant group-by-time interaction revealed diverging emotional trajectories: the AI group sustained positive emotions including enjoyment while decreasing negative emotions such as anxiety, whereas the control group exhibited opposite trends. Post-intervention, while intrinsic load was comparable, the AI group reported significantly lower extraneous load (\n                    <jats:italic>M<\/jats:italic>\n                    \u2009=\u20093.2 vs. 5.4,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.05) and significantly higher germane load (\n                    <jats:italic>M<\/jats:italic>\n                    \u2009=\u20098.1 vs. 5.9,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.05). These findings provide strong evidence that a well-designed AI Digital Teacher can not only substantially improve academic outcomes but also foster a more positive affective environment and optimize cognitive processing for deep learning in a complex humanities domain.\n                  <\/jats:p>","DOI":"10.1186\/s40561-026-00454-0","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T06:01:20Z","timestamp":1779256880000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The impact of an AI Digital Teacher on human-AI collaborative learning in higher education"],"prefix":"10.1186","volume":"13","author":[{"given":"Yili","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1734-7214","authenticated-orcid":false,"given":"Fei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"issue":"8","key":"454_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.20525\/ijrbs.v13i8.3507","volume":"13","author":"O. 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