Theoretical Framework
This chapter presents the two-layer theoretical framework that guides the study. Because the analysis spans three levels — national/institutional, school/instructor, and individual student — no single theory can explain the full range of phenomena. A macro layer provides the overarching integrative structure; an operational layer supplies RQ-specific explanatory mechanisms.
Overview: Two-Layer Theory Structure
| Layer | Unit of Analysis | RQs | Theories |
|---|---|---|---|
| Macro | Whole study | RQ1–RQ5 | Bloom’s Revised Taxonomy + IEA Curriculum Framework |
| National/Institutional | Cross-country comparison | RQ1, RQ2 | Comparative Education Theory, fsQCA Configurational Theory |
| School/Instructor | Pedagogical gap | RQ3, RQ4 | Dynamic Capabilities Theory, TPB |
| Individual | Student perception | RQ5 | TAM, SDT |
Layer 1 — Macro Theories (Study-Wide)
Intended–Enacted–Achieved (IEA) Curriculum Framework
Source: Goodlad (1979); Van den Akker (2003)
The IEA framework is the raison d’être of this study. Hwang et al. (2025) analyzed only the Intended Curriculum (documents) and acknowledged this as an explicit limitation. This study aims to be the first AACSB-based research to address all three curriculum layers.
| Layer | Definition | Position in This Study |
|---|---|---|
| Intended | What curriculum documents prescribe | Phase 1 (document coding, QCA) |
| Enacted | What instructors actually teach | Phase 2a (instructor interviews) |
| Achieved | What students actually learn | Phase 2b (student surveys) |
Theoretical contribution: Among the 55 papers reviewed in the SLR, none explicitly applies the IEA framework to AI–CT integration research. This constitutes the core originality of this study.
Bloom’s Revised Taxonomy
Source: Anderson & Krathwohl (2001)
Bloom’s Revised Taxonomy serves as the common language for measuring CT level across all data sources. It is the most frequently cited theory in the SLR corpus (4 papers) and was adopted by Hwang et al. (2025), ensuring continuity and comparability with the baseline study.
| Bloom’s Level | CT Level | Coding Criterion |
|---|---|---|
| Remembering, Understanding, Applying | Low (L) | Accepting/utilizing AI outputs |
| Analyzing | Mid (M) | Analyzing/applying AI-generated content |
| Evaluating, Creating | High (H) | Critiquing AI outputs, generating alternatives, leading debate |
The taxonomy bridges the Phase 1 coding condition variable (BLOOM_LEVEL) and the Phase 2b student survey (HOTS scale).
Layer 2 — Operational Theories (RQ-Specific)
RQ1: Cross-National Patterns — Comparative Education Theory
Source: Bray & Thomas (1995) Multi-level Comparative Framework
This theory provides the framework for explaining differences in AI–CT integration patterns across countries. It is absent from the 55-paper SLR corpus, which reflects the single-country dominance of prior research (primarily US or China). Adopting this framework is itself a theoretical contribution, providing the rationale for a multi-country comparative design.
- Analytical dimensions: national level (AI policy intensity, AACSB maturity), institutional level (school type, program), individual level (instructors and students)
- Role in QCA: Theoretical justification for the
COUNTRYcondition variable
RQ2: Conditions for Explicit CT Integration — fsQCA Configurational Theory
Source: Ragin (2008) Redesigning Social Inquiry
This methodological theory analyzes “what combinations of conditions lead to Explicit CT Integration.” Only 2 papers in the SLR use fsQCA (both single-country, China). Multi-country application is a first for this research.
- Key concept: Equifinality — different condition combinations leading to the same outcome. The US, Korea, and China may reach the same level of CT integration through different pathways.
- Identifies sufficient condition paths and deviant cases → theoretical basis for Phase 2 sample selection
RQ3: Institutional Variation in Intended–Enacted Gap — Dynamic Capabilities Theory
Source: Teece, Pisano & Shuen (1997)
Why do some schools show a large gap between Intended (documents) and Enacted (actual teaching) curricula, while others do not? An institution’s dynamic capabilities (sensing, seizing, reconfiguring) determine the size of this gap.
- SLR frequency: 1 paper (Gong et al., 2025)
- Can be used as a coding frame for Phase 2a interviews: does the instructor have the capacity to change behavior?
RQ4: Instructor Pedagogical Intention — Theory of Planned Behavior (TPB)
Source: Ajzen (1991)
TPB is the standard model for predicting intentional behavior — in this case, an instructor’s deliberate decision to incorporate AI–CT integration into the curriculum. SLR frequency: 2 papers (including Nowinski et al., 2025).
- Components: Attitude + Subjective Norm + Perceived Behavioral Control → Behavioral Intention
- Directly applicable as a structuring tool for Phase 2a interviews: “Why does this instructor explicitly teach CT (or not)?”
RQ5: Student CT Perception (Achieved) — TAM + SDT
Sources: Davis (1989) TAM; Ryan & Deci (2000) SDT
Two complementary theories for measuring student-level AI–CT perception and learning outcomes.
| Theory | Role | SLR Frequency |
|---|---|---|
| TAM (Technology Acceptance Model) | Pathway from AI tool acceptance to CT utilization | 4 papers |
| SDT (Self-Determination Theory) | Intrinsic motivation (autonomy, competence, relatedness) mediates HOTS attainment | 3 papers |
- Provides the theoretical basis for Phase 2b survey scale construction
- Cross-national comparison: cultural differences in autonomy and competence may be reflected in CT perception differences
Background Contextual Theories (Supplementary)
Dynamic Capabilities (National System Level)
Applies Dynamic Capabilities to national AI education policy capacity. Supplements theoretical description of US–Korea–China policy maturity differences.
Defense Logic
Q: “Why so many theories?”
This study analyzes at three distinct levels: national (RQ1–2), school (RQ3), and individual (RQ4–5). Because different mechanisms operate at each level, level-specific theories are required. The IEA Framework and Bloom’s Taxonomy provide the integrative macro structure; the remaining theories operate only at their respective levels.
Q: “Why isn’t the IEA Framework a mainstream theory?”
Prior literature (55 papers) focused exclusively on Intended Curriculum analysis and therefore had no need for the IEA Framework. This study is the first to include Enacted and Achieved layers in an AACSB-based design; adopting this framework is itself the theoretical contribution.
Q: “Can Comparative Education Theory be justified if it is absent from the literature?”
Its absence from the literature strengthens the case for adopting it. Designing a multi-country comparative study without a comparative education theory framework would actually weaken the methodological justification.
Theory–RQ–Measurement Mapping
| RQ | Core Theory | Phase | Measurement |
|---|---|---|---|
| RQ1: AI–CT integration patterns by country | Bloom’s Taxonomy + Comparative Education Theory | Phase 1 | Document coding, cross-national cluster comparison |
| RQ2: Condition combinations for Explicit CT integration | fsQCA Configurational Theory | Phase 1 | QCA with 6 condition variables |
| RQ3: Intended–Enacted gap | IEA Framework + Dynamic Capabilities | Phase 2a | Instructor interviews + triangulation |
| RQ4: Instructor intention mechanisms | TPB | Phase 2a | Interview coding (attitude, norm, PBC) |
| RQ5: Student CT perception (cross-national) | IEA Framework (Achieved) + TAM + SDT | Phase 2b | Student survey (Likert + HOTS scale) |
References
- Goodlad, J. I. (1979). Curriculum inquiry: The study of curriculum practice. McGraw-Hill.
- Van den Akker, J. (2003). Curriculum perspectives: An introduction. In Curriculum landscapes and trends (pp. 1–10). Springer.
- Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing. Longman.
- Bray, M., & Thomas, R. M. (1995). Levels of comparison in educational studies. Harvard Educational Review, 65(3), 472–490.
- Ragin, C. C. (2008). Redesigning social inquiry. University of Chicago Press.
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
- Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68–78.
- Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
- Hwang, T., Park, B., Kim, H., & Kim, S. (2025). AI and critical thinking in business education: A first mapping of AACSB schools in the USA. Journal of Business and Management Cases.
Social Construction of Technology (SCOT)
Source: Bijker, Hughes & Pinch (1987); Gupta et al. (2024)
Explains why the same AI technology is educationally interpreted differently across countries. Supplements the socio-cultural explanation of national-level pathway differences.