I need Chapter 3 of my dissertation written using the document provided for Chapter 1 & 2. In chapter 3, define the methodology, including research design, data collection and analysis plan.
Title: Methodology: Understanding Instructors’ Perceptions of AI in Adult Learning
3.1 Introduction (~1 page)
- Briefly restate research purpose and questions.
- Explain why a qualitative case study approach is ideal for exploring perceptions of AI.
- Outline what the reader can expect in this chapter.
3.2 Research Design (~2 pages)
- Define qualitative methodology and its relevance to exploratory research.
- Describe and justify the use of an interpretivist paradigm and case study method (Stake, Merriam, Yin).
- Bounded case: Laurus College instructors during AI adoption era.
3.3 Theoretical and Conceptual Framework Alignment (~1.5 pages)
- Reiterate the dual framework:
- Technology Acceptance Model (TAM)
- Constructivist Learning Theory
- Connect how these frameworks shaped interview protocol, coding, and analysis.
- Show alignment with research questions.
3.4 Research Questions (~0.5 page)
List the two key questions:
- What are instructors’ perceptions of generative AI in adult education, particularly in relation to the TAM model components (perceived usefulness, ease of use, and ethical considerations)?
- How do institutional factors influence instructors’ attitudes toward AI integration in adult education settings?
3.5 Role of the Researcher and Reflexivity (~2 pages)
- Detail your positionality as a digital learning researcher and external observer.
- Discuss strategies for managing bias: bracketing, reflexive journaling, and member checking.
- Explain how your background informs interpretation but does not guide outcomes.
3.6 Research Setting and Context (~1 page)
- Describe Laurus College: mission, population (adult learners), and relevance to AI adoption.
- Summarize current state of AI integration efforts and institutional challenges.
- Situate the case within broader national AI-in-education trends.
3.7 Sampling Strategy and Participant Selection (~2 pages)
- Justify purposive sampling of ~15 instructors.
- Include inclusion/exclusion criteria (e.g., familiarity with AI, teaching adult learners).
- Describe recruitment procedures and rationale for sample size (data saturation).
- Highlight participant diversity: subject area, years of experience, AI exposure level.
3.8 Data Collection Methods (~3 pages)
3.8.1 Semi-Structured Interviews
- Explain rationale for semi-structured format.
- Interview logistics: 45–60 minutes, Zoom/in-person, recorded and transcribed.
- Discuss use of sample questions (Appendix reference).
- Ethics: consent, anonymity, voluntary participation.
3.8.2 Document Analysis
- Institutional materials: AI training docs, tech policies, faculty development materials.
- Triangulation with interview data to verify institutional support and alignment.
- Ethical considerations around document sourcing.
3.9 Data Analysis Procedures (~3 pages)
3.9.1 Thematic Analysis Steps (Braun & Clarke)
- Familiarization with data
- Open coding (manual + software-assisted)
- Theme generation and categorization
- Theme review, definition, and naming
- Synthesis of findings using participant quotes
3.9.2 Connection to Frameworks
- Map codes to TAM (PU, PEOU, ethics) and constructivist ideas (scaffolding, active learning).
- Address emergent vs a priori themes.
3.10 Trustworthiness and Rigor (~1.5 pages)
Following Lincoln & Guba:
- Credibility: Member checks, triangulation
- Transferability: Rich, thick description of context and participants
- Dependability: Audit trail, codebook
- Confirmability: Reflexivity, researcher journaling
3.11 Ethical Considerations (~1.5 pages)
- IRB approval process
- Informed consent procedures
- Anonymity and data protection (e.g., encrypted file storage)
- Right to withdraw at any time
- Minimizing psychological and reputational harm
3.12 Researcher Assumptions (~1 page)
- Instructors have basic AI literacy
- Perceptions vary by discipline and experience
- Participants are honest and reflective
- Institutional support influences adoption
- Constructivism and TAM appropriately frame AI integration issues
3.13 Limitations and Delimitations (~1 page)
- Focused on one institution (Laurus College)
- Instructor-only viewpoint (excludes student perspectives)
- Generative AI emphasis (excludes predictive AI)
- May not generalize to non-profit or public higher ed contexts
3.14 Chapter Summary and Transition (~0.5 pages)
- Recap methodology and reinforce credibility of design
- Preview transition to Chapter 4: Data Presentation and Analysis