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Evaluating AI-Enhanced Collaboration in EFL Academic Writing: A Longitudinal Mixed-Methods Study

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Title: Evaluating AI-Enhanced Collaboration in EFL Academic Writing: A Longitudinal Mixed-Methods Study
Language: English
Authors: Wei Guan (ORCID 0009-0009-6202-8109), Akbar Bahari (ORCID 0000-0002-4575-6480)
Source: Journal of Educational Computing Research. 2026 64(4):951-988.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 38
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Technology Uses in Education, English (Second Language), Second Language Learning, Academic Language, Writing Achievement, Student Attitudes, Blended Learning, Learner Engagement, Writing Skills, Technology Integration
DOI: 10.1177/07356331251404736
ISSN: 0735-6331
1541-4140
Abstract: This longitudinal mixed-methods investigation addressed the unresolved question of how different modes of AI-enhanced collaboration shape EFL academic writing across content enrichment, linguistic accuracy, and organizational coherence. A four-arm, stratified, parallel-group randomized controlled trial (Randomized Controlled Trial [RCT]) with concealed, centralized allocation and assessor blinding was implemented over an 8-week hybrid program (2 h in-person +2 h online weekly; 32 contact hours). Randomization used permuted blocks (size = 8; 1:1:1:1) within university, baseline proficiency tertiles, and gender strata; analyses followed intention-to-treat with multiple imputation (m = 50; Rubin's rules) and prespecified sensitivity checks (complete-case, per-protocol). Quantitatively, outcomes were captured via rubric-based assessments, automated writing analytics, and AI engagement indices; qualitatively, semi-structured interviews (n = 88) and validated surveys were analyzed through reflexive thematic analysis. Integration occurred at design, methods, and interpretation using joint displays and triangulation. The content-enrichment intervention produced the largest and most durable gains (e.g., d > 0.80), including a substantial adjusted mean difference in overall writing versus control (Δ = 1.95, p < 0.001). Multivariate analyses (MANCOVA) showed a significant group effect after adjusting for pretest scores (p < 0.001). Path analysis indicated that perceived AI utility and digital literacy significantly mediated performance gains. Thematically, AI functioned as a collaborative partner, though dense feedback sometimes elevated cognitive load.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1502176
Database: ERIC
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  Data: Evaluating AI-Enhanced Collaboration in EFL Academic Writing: A Longitudinal Mixed-Methods Study
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wei+Guan%22&quot;&gt;Wei Guan&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0009-0009-6202-8109&quot;&gt;0009-0009-6202-8109&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Akbar+Bahari%22&quot;&gt;Akbar Bahari&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0000-0002-4575-6480&quot;&gt;0000-0002-4575-6480&lt;/externalLink&gt;)
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: This longitudinal mixed-methods investigation addressed the unresolved question of how different modes of AI-enhanced collaboration shape EFL academic writing across content enrichment, linguistic accuracy, and organizational coherence. A four-arm, stratified, parallel-group randomized controlled trial (Randomized Controlled Trial [RCT]) with concealed, centralized allocation and assessor blinding was implemented over an 8-week hybrid program (2 h in-person +2 h online weekly; 32 contact hours). Randomization used permuted blocks (size = 8; 1:1:1:1) within university, baseline proficiency tertiles, and gender strata; analyses followed intention-to-treat with multiple imputation (m = 50; Rubin&#39;s rules) and prespecified sensitivity checks (complete-case, per-protocol). Quantitatively, outcomes were captured via rubric-based assessments, automated writing analytics, and AI engagement indices; qualitatively, semi-structured interviews (n = 88) and validated surveys were analyzed through reflexive thematic analysis. Integration occurred at design, methods, and interpretation using joint displays and triangulation. The content-enrichment intervention produced the largest and most durable gains (e.g., d &gt; 0.80), including a substantial adjusted mean difference in overall writing versus control (Δ = 1.95, p &lt; 0.001). Multivariate analyses (MANCOVA) showed a significant group effect after adjusting for pretest scores (p &lt; 0.001). Path analysis indicated that perceived AI utility and digital literacy significantly mediated performance gains. Thematically, AI functioned as a collaborative partner, though dense feedback sometimes elevated cognitive load.
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