AI evidence extraction

Theoretical Framework for an AI-Enhanced Pedagogical Technology to Develop Self-Editing Skills of Doctoral Students

Ksenia Volchenkova, O.R.A. Alhato · Bulletin of the South Ural State University series Education Education Sciences · 2026

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9/10

Relevance

0/4

Quality (LMQS)

Evidence

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FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.14529/ped260108

Methodology & findings

Study design

Integrative Literature Review (ILR) combined with systematic tool evaluation.

Primary method

Design science research; system and activity-based design approaches

Main result

The study offers an AI-enhanced pedagogical technology by defining the stages of self-editing skills development, namely, substantive editing, copyediting, and proofreading, and mapping selected AI-enhanced writing tools to each stage within a pedagogical system based on reflective approach, collaborative approach, and self-directed learning. The authors "identified and mapped existing AI-enhanced writing tools to develop self-editing skills" and "defined the methodology for the AI-enhanced pedagogical technology" through systematic evaluation of over 70 AI tools, ultimately selecting 15 tools suited to the three self-editing stages.

Research paradigm

Design science / instructional design

Author conclusions

The authors conclude that "The study offers an AI-enhanced pedagogical technology by defining the stages of self-editing skills development, namely, substantive editing, copyediting, and proofreading, and mapping selected AI-enhanced writing tools to each stage within a pedagogical system based on reflective approach, approach, and self-directed learning. The theoretical framework to develop self-editing skills of PhD students is adaptable across disciplines, aiming to enhance research dissemination and prepare autonomous scholars for the evolving digital academic landscape."

Risk of bias

Selection bias in AI tool assessment (based on published features rather than comparative efficacy studies); Lack of empirical validation in real educational settings; Limited longitudinal data on educational impact; No empirical validation - theoretical framework only; Tool selection based on published features rather than comparative efficacy studies; Limited to tools listed on AIxploria platform; No longitudinal data on educational impact; ILR methodology may have selection bias in articles chosen; No empirical validation - theoretical framework only, no user testing conducted; Limited to 20 research papers from literature review of 120 articles - potential selection bias in what qualifies as 'leading scholars'; No longitudinal data on tool effectiveness; Authors did not test the framework in actual doctoral education contexts

Limitations

The authors state: "First, the pedagogical technology is presented as a theoretical model and has not yet been empirically validated through implementation in a doctoral training context
Second, the selection of AI-enhanced writing tools was based on an analysis of their published features and functionalities, not on comparative efficacy studies or longitudinal data on their educational impact." The framework remains untested in actual educational settings.

Open questions raised

Lack of empirical validation of the pedagogical technology in real doctoral training contexts
Need for comparative efficacy studies of AI-enhanced writing tools
Future research needed to measure impact on quality of students' academic writing and development of autonomous scholarly habits
Investigation of how technology integration influences dynamics between doctoral students and supervisors regarding feedback practices and critical GAI literacy development
Need for empirical validation of the framework through implementation in actual doctoral training contexts
Investigation of the framework's effectiveness in measuring impact on quality of students' academic writing, self-editing proficiency, and development of autonomous scholarly habits

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