Methodology

This study employs a Sequential Explanatory Mixed Methods Design (Creswell, 2003), built as an extension of Hwang et al. (2025). Phase 1 discovers patterns in Intended Curriculum across three countries; Phase 2 explains those patterns by examining Enacted and Achieved Curriculum through qualitative and quantitative methods.

Phase 1 (QUAN/QUAL)             →          Phase 2 (QUAL + QUAN)
Intended Curriculum                        Enacted + Achieved
Document coding + QCA                      Interviews + Surveys
"What are the patterns?"                   "Why those patterns?"
Full sample (~80 schools)                  Purposive sub-sample (~15–30 schools)

Design Rationale

Why a Two-Phase Design?

Rationale	Explanation
Comparability with existing research	Phase 1 operates at the same level (Intended) as Hwang et al., enabling direct comparison with the US baseline
Phase 1 informs Phase 2	QCA results (pathways and deviant cases) provide the theoretical basis for Phase 2 sample selection
Illuminates the Intended–Enacted gap	Addresses the core question: “The document says Explicit CT — but is it actually taught?”
Multiple publication outputs	Phase 1 = standalone paper; Phase 1+2 = second paper

Curriculum Layers and Phase Mapping

Curriculum Layer	Definition	Phase	Data
Intended	What the curriculum document prescribes	Phase 1	Public document coding
Enacted	What actually happens in the classroom	Phase 2a	Instructor interviews
Achieved	What students actually learn	Phase 2b	Student surveys

This three-layer structure follows the IEA Curriculum Framework (Goodlad, 1979; Van den Akker, 2003), which is absent from all 55 papers reviewed in the SLR — constituting the core theoretical originality of this study.

Phase 1: Intended Curriculum — International Comparison + Pathway Identification

Purpose

Map AI–CT integration patterns in AACSB-accredited business schools across US, China, and Korea
Identify condition combinations (pathways) for Explicit CT Integration via QCA
Triangulate findings with Topic Modeling and Network Analysis

Sample

Country	N	Basis
US	40	Reuse of Hwang et al. (2025) data
China	20–30	AACSB-accredited schools, filtered by global ranking + data availability
Korea	15–20	Near-census of all AACSB-accredited schools

Methods

Content Analysis: Apply Hwang et al.’s 4-dimension coding framework identically
Comparative Mapping: Derive country-specific clusters; compare across countries
QCA (fsQCA): Identify condition combinations leading to Explicit CT Integration
Topic Modeling (STM): Inductively discover latent themes; prevalence ~ country + program_level
Network Analysis: Visualize co-occurrence patterns of coding elements; compare by country

Phase 1 Outputs

Cross-national cluster distribution comparison table
QCA sufficient-condition paths (including country-specific paths)
List of deviant cases → criteria for Phase 2 sample selection

Research Questions (Phase 1)

RQ1: How do AI–CT integration patterns differ across US, China, and Korea among AACSB-accredited business schools?
RQ2: What combinations of conditions lead to Explicit CT Integration, and do country-specific pathways exist?

Phase 1 → Phase 2 Connection

Phase 2 sampling is theoretically informed by Phase 1 QCA results (purposive sampling logic).

Phase 1 QCA Results
  │
  ├── Path A: Active Pedagogy × Strategy Theme → Explicit CT  (predominantly US)
  ├── Path B: AI Policy × Theme Diversity → Explicit CT        (predominantly China)
  ├── Path C: Weak or undiscovered path                        (Korea)
  └── Deviant cases: Schools with the conditions but unexpected outcomes
  │
  ▼
Phase 2 Sample Selection (Purposive Sampling)
  │
  ├── 2–3 typical cases from each path
  ├── 2–3 deviant cases
  └── Balanced country allocation (e.g., US 5, China 5, Korea 5)

Phase 2a: Enacted Curriculum — Instructor Interviews

Purpose

Identify the gap between Intended (documents) and Enacted (actual teaching) curriculum
Qualitatively explain the causal mechanisms underlying QCA pathways
Explore the origins of deviant cases

Participants

Instructors of AI-integrated courses
5–10 per country; selected based on Phase 1 QCA pathways

Method

Semi-structured interviews, 45–60 minutes (online or in-person)

Interview Guide — Core Question Areas

Domain	Sample Question	Phase 1 Dimension
AI integration decision-making	“What led you to integrate AI into the curriculum?”	Curricular Themes
Actual teaching practice	“How do students interact with AI in class?”	Pedagogical Approaches
CT facilitation strategies	“What do you intentionally do to promote critical thinking?”	CT Level / CT Linkage
Intended–Enacted gap	“Is there a gap between what the syllabus says and what actually happens?”	Coding validity
Assessment & feedback	“How do you assess CT? Has AI use changed this?”	Assessment Modes
Contextual factors	“How have school policy, accreditation requirements, or national policy influenced AI integration?”	QCA contextual conditions

Analysis

Thematic Analysis: Braun & Clarke (2006) 6-step procedure
Deductive–inductive hybrid coding against Phase 1 framework

Research Questions (Phase 2a)

RQ3: What is the gap between AI–CT Intended curriculum (documents) and Enacted curriculum (teaching practice), and what causes it?
RQ4: How do instructor-perceived facilitating and inhibiting factors for AI–CT integration differ by country?

Phase 2b: Achieved Curriculum — Student Survey

Purpose

Measure CT experience and perception of students enrolled in AI-integrated courses
Triangulate the full Intended → Enacted → Achieved curriculum chain
Test whether Phase 1 cluster membership predicts student perception differences

Participants

Students enrolled in courses taught by Phase 2a interviewees
30–50 per course; total estimated 300–500

Method

Online survey (Likert scale + open-ended items)

Survey Structure

Section	Content	Instrument
A. AI usage experience	Frequency and mode of AI use in class	Self-developed (frequency, type, tools)
B. CT self-perception	Perceived impact of AI use on critical thinking	Adapted CT self-efficacy scale
C. CT skills (optional)	Indirect measurement of actual CT ability	Watson-Glaser short form or CCTST subscale
D. Pedagogical experience	Teaching methods and assessment experienced	Student version of Hwang et al. coding framework
E. Contextual awareness	AI policy, school support, cultural factors	Self-developed

Analysis

Descriptive statistics + cross-national comparison (ANOVA / Kruskal-Wallis)
Test CT perception differences by Phase 1 cluster membership
Triangulation: Enacted (instructor interviews) ↔︎ Achieved (student surveys)

Research Question (Phase 2b)

RQ5: How do CT experience and perception of students enrolled in AI-integrated courses differ by country and cluster?

Integrated Analysis Framework

                    Intended           Enacted            Achieved
                    (Phase 1)          (Phase 2a)         (Phase 2b)
                    Document Coding    Instructor Interviews  Student Surveys
─────────────────────────────────────────────────────────────────────────────
Curricular Themes   Course description → "Why this theme?"   "How did you use AI?"

Pedagogy            Syllabus coding  → "How do you          "Did class activities
                                       actually teach?"      help your CT?"

Assessment          Assessment items → "How do you assess    "Was assessment fair
                    coding             CT?"                  and CT-reflective?"

CT Level/Linkage    Bloom's mapping  → "What is your         CT self-efficacy
                    + explicitness      intentional CT        + CT test scores
                    check               strategy?"

National Context    AI policy coding → "Did policy/          "Did the national AI
                                       accreditation          environment affect
                                       influence you?"        your learning?"

Publication Strategy

Paper	Content	Phase	Target Journal Type
Paper 1	International Comparative Mapping + QCA	Phase 1	Business education journals (IJME, JME, DSJIE)
Paper 2	Mixed Methods: Intended–Enacted–Achieved Triangulation	Phase 1+2	Higher education journals (Studies in Higher Education, Higher Education, AAHE)
Paper 3 (optional)	Topic Modeling + Network Analysis methodology paper	Phase 1 deep dive	Methods/ed-tech journals (Educational Research Review, C&E)

Timeline

Period	Task	Phase
2026-04 to 06	Data collection, coding, Comparative Mapping	Phase 1
2026-06 to 07	QCA + Topic Modeling + Network Analysis	Phase 1
2026-07 to 08	Paper 1 writing + submission	Phase 1
2026-07 to 08	IRB approval, interview guide/survey development, pilot	Phase 2 prep
2026-09 to 11	Interview + survey data collection	Phase 2
2026-12 to 2027-02	Phase 2 analysis + Paper 2 writing	Phase 2

IRB Considerations (Phase 2)

Item	Details
When needed	Before Phase 2 data collection (both interviews and surveys)
Applying institution	Research lead university IRB
International considerations	Local IRB or partner institution approval required for China/Korea data collection
Risk level	Minimal risk (educational research, anonymous/de-identified)
Consent forms	Instructor interview consent + student survey consent (multilingual)

References

Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Sage.
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.
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.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.