Data · dataset · 2026
Pre-service Teachers’ Experiences and Competence Development in AI agent-supported Interdisciplinary Lesson Planning: A Mixed Study
Listed in ScienceDB
Questionnaire: Zhu’s (2023) Teacher Multidisciplinary Competence Scale was used to employ the pre-test and post-test.
Description
This 21-item instrument includes three subscales, namely pedagogical knowledge (6 items), teaching competencies (9 items), and teaching affections (6 items). Responses to the questionnaire items were scored using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).Reflection: The qualitative design employed for this research study involved the use of a pre-post comparative lesson planning reflection guide generated by the authors.
Participants were expected to carry out independent interdisciplinary lesson planning before using an AI agent for the revision of their lesson plans. In the subsequent stage, participants were supposed to reflect on both their initial and revised interdisciplinary lesson plans. The set of reflective questions posed to participants included the following: 1.
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What are the differences that you have observed between your initial interdisciplinary lesson plan and your revised AI-agent-assisted interdisciplinary lesson plan in regard to the four main parts of a lesson: introduction, delivery, activities and practice, and conclusion? 2. How has the AI agent affected your practice of interdisciplinary lesson planning in regard to the above-mentioned four parts of a lesson?
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.012ou ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Curriculum and pedagogy 70%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.012ou | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3901 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (70%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |