Research Proposal: AI Readiness of Teachers and Lecturers in Indonesian Higher Education

Oleh galihwono 27 Nov, 2025

Research Proposal: AI Readiness of Teachers and Lecturers in Indonesian Higher Education

Proposed Doctoral Research in Computer Science

Executive Summary

This doctoral research proposal aims to investigate the artificial intelligence (AI) readiness of teachers and lecturers in Indonesian higher education institutions. As Indonesia undergoes rapid digital transformation in education, understanding educators' preparedness to integrate AI technologies is critical for effective implementation and policy development. This study will employ a mixed-methods approach to assess AI readiness across four key dimensions: cognitive understanding, technical ability, vision for AI integration, and ethical considerations. The research will provide the first comprehensive, multi-institutional analysis of AI readiness in Indonesian higher education, contributing both theoretical insights and practical recommendations for national education policy.

1. Introduction

1.1 Background and Context

The integration of artificial intelligence in education represents one of the most significant transformations in contemporary higher education. AI technologies—including intelligent tutoring systems, automated assessment tools, personalized learning platforms, and generative AI applications—are reshaping pedagogical practices worldwide [1][2]. However, the successful implementation of AI in education depends critically on educators' readiness to adopt and effectively utilize these technologies [3].

Indonesia, as the fourth most populous country and the largest economy in Southeast Asia, faces unique challenges and opportunities in AI integration. With over 4,500 higher education institutions serving approximately 8 million students, the Indonesian higher education system is undergoing significant digital transformation initiatives [4]. The Indonesian government has prioritized digital literacy and technology integration through policies such as the "Making Indonesia 4.0" roadmap and the "Kampus Merdeka" (Independent Campus) policy, which emphasizes innovation and technology adoption [5].

Despite these national initiatives, empirical evidence regarding the actual readiness of Indonesian educators to integrate AI technologies remains limited. Most existing research on AI readiness in education has been conducted in developed countries, particularly China, the United States, and European nations, with minimal attention to Southeast Asian contexts [6][7]. This research gap is particularly concerning given that Indonesia's educational landscape differs significantly from developed countries in terms of infrastructure, resources, institutional support, and cultural contexts [8].

1.2 Problem Statement

The rapid advancement and increasing accessibility of AI technologies have created both opportunities and challenges for Indonesian higher education. While institutional leaders and policymakers advocate for AI integration, the actual readiness of frontline educators—those who will implement AI tools in their teaching practice—remains largely unknown. This knowledge gap creates several critical problems:

Policy-Practice Disconnect: National policies promoting AI integration may not align with educators' actual capabilities and readiness levels
Resource Misallocation: Without understanding current readiness levels, institutions may invest in AI technologies that educators cannot effectively utilize
Inequitable Implementation: Regional and institutional variations in readiness may lead to unequal AI adoption, exacerbating existing educational disparities
Missed Opportunities: Lack of understanding about factors influencing readiness prevents the development of targeted interventions to enhance adoption

1.3 Research Significance

This research is significant for several reasons:

Theoretical Significance: This study will be among the first to validate and potentially extend existing AI readiness frameworks in a Southeast Asian context, contributing to the global understanding of technology adoption in diverse cultural and institutional settings.

Empirical Significance: The research will provide the first comprehensive, multi-institutional baseline data on AI readiness among Indonesian educators, enabling future longitudinal studies and comparative analyses.

Practical Significance: Findings will directly inform national education policy, institutional strategies, and professional development programs, ensuring that AI integration efforts are grounded in empirical evidence rather than assumptions.

Regional Significance: As Indonesia is often considered a bellwether for Southeast Asian education trends, this research may provide insights applicable to other developing countries in the region facing similar challenges.

2. Literature Review

2.1 Conceptualizing AI Readiness in Education

AI readiness in educational contexts has emerged as a multidimensional construct encompassing educators' preparedness to adopt, integrate, and effectively utilize AI technologies in teaching and learning [9]. Unlike general technology acceptance, AI readiness specifically addresses the unique characteristics of AI systems—including their adaptive nature, data-driven decision-making, and potential to fundamentally reshape pedagogical approaches [10].

2.1.1 Theoretical Frameworks

Several theoretical frameworks have been employed to understand AI readiness:

Technology Acceptance Model (TAM) and Extensions: The TAM framework, which emphasizes perceived usefulness and ease of use as key determinants of technology adoption, has been extended to AI contexts [11]. Studies have validated TAM-based models showing that educators' intentions to adopt AI tools are significantly influenced by perceived pedagogical benefits and facilitating conditions [12].

Unified Theory of Acceptance and Use of Technology (UTAUT): UTAUT has been deployed to link perceived benefits, social influence, and facilitating conditions with AI adoption readiness among faculty [13]. This framework has proven particularly useful for understanding institutional and social factors affecting adoption.

AI Readiness Framework: Recent research has proposed specific AI readiness frameworks comprising four key dimensions [14]:

Cognitive Readiness: Understanding of AI functions, capabilities, and limitations
Ability/Competence: Technical skills and pedagogical competence in implementing AI
Vision: Perceptions of AI's opportunities and challenges in education
Ethical Considerations: Understanding of ethical and legal frameworks governing AI use

Contextual Readiness Models: These approaches evaluate institutional policies, infrastructure, and faculty competence within specific national and cultural contexts [15], recognizing that readiness is not solely an individual attribute but is shaped by systemic factors.

2.2 Factors Influencing AI Readiness

Empirical research has identified several key factors that influence educators' AI readiness:

Individual Factors:

Technological innovativeness: Educators' personal openness to innovation and technology experimentation significantly predicts readiness [16]
Self-efficacy: Confidence in one's ability to learn and use AI tools correlates with adoption intentions [17]
Value-cost beliefs: Perceived benefits relative to required effort influence willingness to integrate AI [18]

Institutional Factors:

Training and professional development: Availability of targeted AI training programs strongly influences readiness levels [19]
Institutional support: Clear policies, technical support, and leadership commitment facilitate adoption [20]
Access to tools: Availability of AI technologies and resources is a fundamental enabler [21]

Contextual Factors:

Cultural attitudes: Cultural perceptions of technology and education shape adoption patterns [22]
Infrastructure: Digital infrastructure and connectivity affect feasibility of AI integration [23]
Policy environment: National and institutional policies create enabling or constraining conditions [24]

2.3 AI in Education: Global Perspectives

Globally, AI integration in higher education has progressed unevenly. Developed countries, particularly China, the United States, and several European nations, have made substantial investments in educational AI and have higher levels of institutional readiness [25]. Research from these contexts shows that successful AI integration requires not only technological infrastructure but also systematic faculty development, ethical guidelines, and pedagogical innovation [26].

In contrast, developing countries face additional challenges including limited infrastructure, resource constraints, and gaps in digital literacy [27]. Studies from African and Asian contexts highlight that while educators may be enthusiastic about AI, actual readiness levels are often constrained by systemic barriers [28].

2.4 AI Readiness in Southeast Asia and Indonesia

Research on AI readiness in Southeast Asian education is notably limited. A few studies have examined specific aspects:

A study of Indonesian and Indian EFL instructors found moderate readiness levels but highlighted significant training needs and concerns about ethical implications [29]
Research on AI integration in Indonesian education identified challenges related to infrastructure, policy implementation, and faculty competence [30]
Comparative studies suggest that AI adoption in Southeast Asian higher education is "uneven but accelerating," with significant variation across and within countries [31]

2.5 Research Gaps

The literature review reveals several critical gaps:

Limited Regional Coverage: Most AI readiness research focuses on developed countries; Southeast Asia, including Indonesia, is significantly underrepresented [32]
Small-Scale Studies: Existing Indonesian studies are typically single-institution or small-sample efforts, limiting generalizability [33]
Lack of Comprehensive Frameworks: Few studies have applied multidimensional AI readiness frameworks in Indonesian contexts or validated such frameworks for cultural appropriateness [34]
Cross-Sectional Limitations: Predominance of snapshot surveys rather than longitudinal or intervention studies limits understanding of readiness development over time [35]
Policy-Practice Gap: Limited empirical work assessing how AI readiness indicators map into national curricula or policy implementation at scale [36]
Stakeholder Diversity: Most studies target faculty; fewer examine multiple stakeholders or system-level readiness in developing country settings [37]

This proposed research directly addresses these gaps by providing the first comprehensive, multi-institutional, multidimensional assessment of AI readiness among Indonesian higher education educators.

3. Research Questions and Objectives

3.1 Primary Research Question

What is the current state of AI readiness among lecturers in Indonesian higher education institutions, and what factors influence their readiness to integrate AI technologies in teaching and learning?

3.2 Secondary Research Questions

RQ1: How do the four dimensions of AI readiness (cognitive understanding, technical ability, vision, and ethical considerations) manifest among Indonesian lecturers?
RQ2: What demographic, institutional, and regional factors are associated with variations in AI readiness levels among Indonesian educators?
RQ3: How does AI readiness relate to lecturers' perceived threats from AI, teaching innovation, and job satisfaction?
RQ4: What are the unique cultural and contextual factors affecting AI readiness and adoption in Indonesian higher education?
RQ5: What institutional policies, practices, and support mechanisms most effectively enhance AI readiness among Indonesian educators?

3.3 Research Objectives

To assess the current levels of AI readiness across four dimensions among lecturers in Indonesian higher education institutions
To identify key factors (demographic, institutional, regional, and cultural) that influence AI readiness levels
To examine the relationships between AI readiness and outcomes such as teaching innovation, perceived threats, and job satisfaction
To develop a culturally-validated AI readiness assessment instrument appropriate for Indonesian contexts
To provide evidence-based recommendations for policy makers, institutional leaders, and professional development programs to enhance AI readiness

4. Theoretical Framework

This research adopts an integrated theoretical framework combining elements from technology acceptance theory, organizational readiness theory, and contextual adaptation models.

4.1 Core Theoretical Foundation

The study's theoretical foundation is built on four interconnected dimensions of AI readiness, adapted from recent AI readiness frameworks [14] and contextualized for Indonesian higher education:

4.1.1 Cognitive Readiness Dimension

Definition: Educators' understanding of AI technologies, their functions, capabilities, and limitations in educational contexts.

Components:

Basic AI literacy (understanding what AI is and how it works)
Awareness of AI applications in education
Understanding of AI's role in relation to human teaching
Knowledge of AI's strengths and limitations

4.1.2 Ability/Competence Dimension

Definition: Educators' actual and perceived technical skills and pedagogical competence to implement AI tools effectively.

Components:

Technical skills in using AI tools and platforms
Pedagogical competence in integrating AI into teaching
Ability to evaluate and select appropriate AI tools
Skills in managing AI-enhanced learning environments

4.1.3 Vision Dimension

Definition: Educators' perceptions of AI's potential opportunities, challenges, and future role in education.

Components:

Perceived benefits of AI for teaching and learning
Awareness of challenges and risks
Vision for AI's role in future education
Expectations regarding AI's impact on the profession

4.1.4 Ethical Considerations Dimension

Definition: Educators' understanding of and commitment to ethical principles, privacy concerns, and responsible AI use in education.

Components:

Awareness of ethical issues (bias, fairness, transparency)
Understanding of data privacy and security concerns
Knowledge of legal and regulatory frameworks
Commitment to responsible and equitable AI use

4.2 Influencing Factors

The framework incorporates multiple levels of factors that influence AI readiness:

Individual Level:

Demographics (age, gender, teaching experience, discipline)
Personal innovativeness and technology self-efficacy
Prior experience with educational technology
Attitudes toward change and innovation

Institutional Level:

Type and size of institution (public/private, university/polytechnic)
Availability of AI infrastructure and tools
Professional development and training opportunities
Institutional policies and leadership support
Peer culture and collaborative practices

Contextual Level:

Regional location (urban/rural, Java/outer islands)
Socioeconomic context
National policy environment
Cultural factors and local values

4.3 Outcome Variables

The framework examines relationships between AI readiness and key outcomes:

Teaching innovation and pedagogical practices
Perceived threats from AI (job security, professional identity)
Job satisfaction and professional well-being
Actual AI adoption behaviors

4.4 Conceptual Model

[Individual Factors]
[Institutional Factors]     →    [AI Readiness]    →    [Outcomes]
[Contextual Factors]              - Cognitive              - Teaching Innovation
                                   - Ability                - Perceived Threats
                                   - Vision                 - Job Satisfaction
                                   - Ethics                 - AI Adoption

This integrated framework recognizes that AI readiness is not a static individual attribute but a dynamic construct shaped by multiple interacting factors across different levels of the educational system.

5. Research Methodology

This research will employ a sequential explanatory mixed-methods design, combining quantitative and qualitative approaches to provide comprehensive insights into AI readiness among Indonesian lecturers.

5.1 Research Design

Phase 1: Quantitative Phase (Months 1-18)

Instrument development and validation
Large-scale cross-sectional survey
Statistical analysis (descriptive, inferential, and structural equation modeling)

Phase 2: Qualitative Phase (Months 19-30)

In-depth interviews with selected participants
Focus group discussions
Document analysis of institutional policies
Thematic analysis

Phase 3: Integration and Synthesis (Months 31-36)

Mixed-methods integration
Development of recommendations
Validation with stakeholders
Dissertation writing and dissemination

5.2 Phase 1: Quantitative Research

5.2.1 Population and Sampling

Target Population: Lecturers and faculty members in Indonesian higher education institutions (universities, polytechnics, and academies) across all disciplines.

Sampling Strategy: Multi-stage stratified random sampling

Stage 1 - Regional Stratification:

Major regions: Java, Sumatra, Kalimantan, Sulawesi, Eastern Indonesia
Ensure representation from urban and rural areas

Stage 2 - Institutional Stratification:

Institution type: Public universities, private universities, polytechnics
Institution size: Large (>20,000 students), medium (5,000-20,000), small (<5,000)

Stage 3 - Random Selection:

Random selection of institutions within each stratum
Random or census sampling of faculty within selected institutions

Target Sample Size: Minimum 1,200 respondents

Based on power analysis for structural equation modeling (minimum 10-15 cases per parameter)
Accounting for 40% response rate, target distribution: 3,000 invitations

5.2.2 Instrumentation

AI Readiness Questionnaire - A comprehensive instrument comprising:

Section A: Demographics and Background

Personal information (age, gender, highest degree)
Professional information (teaching experience, discipline, position)
Institutional information (type, location, size)
Technology experience and access

Section B: AI Readiness Dimensions (Likert scale items, 1-5)

Cognitive Readiness Subscale (12 items)

Example items:
- "I understand what artificial intelligence is and how it works"
- "I am aware of various AI applications available for education"
- "I understand the limitations of AI in educational contexts"

Ability/Competence Subscale (15 items)

Example items:
- "I have the technical skills to use AI tools in my teaching"
- "I can evaluate which AI tools are appropriate for my courses"
- "I am confident in integrating AI into my pedagogical practices"

Vision Subscale (12 items)

Example items:
- "AI has the potential to significantly improve student learning"
- "I foresee challenges in implementing AI in my teaching context"
- "AI will play an important role in the future of higher education"

Ethical Considerations Subscale (10 items)

Example items:
- "I am aware of ethical issues related to AI use in education"
- "I understand data privacy concerns when using AI tools"
- "I am committed to using AI in a fair and equitable manner"

Section C: Outcome Variables

Teaching Innovation Scale (8 items)

Measures pedagogical innovation and willingness to experiment

Perceived Threat Scale (6 items)

Assesses concerns about job security and professional identity

Job Satisfaction Scale (5 items)

Evaluates overall job satisfaction

AI Adoption Behaviors (10 items)

Current and intended use of specific AI tools

Section D: Facilitating and Hindering Factors

Institutional support and barriers
Training needs and preferences
Open-ended questions for additional insights

5.2.3 Instrument Validation

Translation and Cultural Adaptation:

Forward and back-translation (English ↔ Bahasa Indonesia)
Expert panel review for cultural appropriateness
Cognitive interviews with 15-20 Indonesian lecturers

Pilot Testing:

Pilot survey with 150-200 lecturers from non-sample institutions
Exploratory factor analysis (EFA) to assess dimensionality
Reliability analysis (Cronbach's alpha, composite reliability)
Item refinement based on pilot results

Validation Study:

Confirmatory factor analysis (CFA) with main sample
Convergent and discriminant validity assessment
Measurement invariance testing across groups

5.2.4 Data Collection Procedures

Survey Administration:

Online survey platform (Qualtrics or similar)
Email invitations through institutional channels
Multiple reminder emails (weeks 2, 4, 6)
Incentive: Entry into lottery for technology vouchers

Data Quality Measures:

Attention check items
Response time monitoring
Validation of institutional affiliations
Screening for incomplete or patterned responses

5.2.5 Data Analysis

Descriptive Analysis:

Frequency distributions, means, standard deviations
AI readiness profiles across dimensions
Visualization of readiness levels by demographics and institutions

Inferential Analysis:

Independent samples t-tests and ANOVA for group comparisons
Post-hoc tests for multiple comparisons
Effect size calculations

Multivariate Analysis:

Multiple regression to identify predictors of AI readiness
Hierarchical regression to assess variance explained by different factor levels

Structural Equation Modeling (SEM):

Partial Least Squares SEM (PLS-SEM) using SmartPLS or similar
Assessment of measurement model (reliability, validity)
Assessment of structural model (path coefficients, R², effect sizes)
Multi-group analysis to test for differences across institutional types, regions, etc.
Mediation and moderation analyses

Software: SPSS 28, SmartPLS 4, R (lavaan package)

5.3 Phase 2: Qualitative Research

5.3.1 Sampling Strategy

Purposive Sampling based on quantitative results:

Maximum variation sampling across readiness levels (high, medium, low)
Representation of different institution types, regions, and disciplines
Target: 40-50 individual interviews, 6-8 focus groups

5.3.2 Data Collection Methods

In-Depth Semi-Structured Interviews (60-90 minutes)

Exploration of personal experiences with AI and technology
Understanding of contextual factors influencing readiness
Perceptions of institutional support and barriers
Vision for AI in Indonesian higher education
Ethical concerns and cultural considerations

Interview Protocol Themes:

Personal AI journey and current understanding
Teaching practices and technology integration
Institutional context and support systems
Cultural and societal factors
Future aspirations and concerns
Recommendations for enhancing readiness

Focus Group Discussions (90-120 minutes, 6-8 participants each)

Disciplinary groups (STEM, Social Sciences, Humanities, etc.)
Institutional type groups (public vs. private)
Regional groups (Java vs. outer islands)
Mixed groups for cross-pollination of ideas

Document Analysis:

Institutional AI and technology integration policies
National education policy documents
Professional development program materials
Strategic plans and mission statements

5.3.3 Data Analysis

Thematic Analysis following Braun & Clarke's approach:

Familiarization with data (transcription, reading, note-taking)
Generating initial codes (using NVivo or ATLAS.ti)
Searching for themes
Reviewing and refining themes
Defining and naming themes
Producing the report

Coding Framework:

Deductive codes from theoretical framework
Inductive codes emerging from data
Attention to cultural and contextual nuances

Quality Assurance:

Member checking with participants
Peer debriefing with research team
Thick description for transferability
Reflexivity and researcher positionality statements

5.4 Mixed-Methods Integration

Integration Strategies:

Connection: Quantitative results inform qualitative sampling and protocol development

Building: Qualitative findings explain and elaborate quantitative patterns

Merging: Side-by-side comparison of quantitative and qualitative results

Joint Display Tables: Visual integration showing convergence, divergence, and complementarity

Meta-Inferences: Higher-order conclusions drawing on both data sources

5.5 Ethical Considerations

Ethical Approval:

Ethics approval from university research ethics committee
Approval from Indonesian Ministry of Education if required
Institutional permissions from participating universities

Informed Consent:

Clear explanation of research purpose and procedures
Voluntary participation with right to withdraw
Separate consent for interviews and focus groups

Confidentiality and Anonymity:

Anonymous survey responses
Pseudonyms for interview participants
Secure data storage and limited access
Institutional anonymity in reporting (unless permission granted)

Data Management:

Encrypted storage of digital data
Secure cloud backup
Data retention and destruction protocols
Compliance with Indonesian data protection regulations

Beneficence and Non-Maleficence:

No deception or harm to participants
Potential benefits clearly communicated
Sensitivity to power dynamics in higher education settings
Culturally respectful research practices

6. Expected Outcomes and Contributions

6.1 Theoretical Contributions

Framework Validation and Extension
- Validation of AI readiness framework in Southeast Asian context
- Potential identification of culturally-specific dimensions or factors
- Contribution to global understanding of technology adoption in diverse settings
Theoretical Model Development
- Empirically-tested model of AI readiness determinants and outcomes
- Understanding of multi-level influences on readiness
- Insights into relationships between readiness dimensions and adoption behaviors
Cross-Cultural Technology Adoption Theory
- Evidence for cultural adaptation of technology acceptance models
- Understanding of how collectivist values influence technology adoption
- Insights into role of institutional context in developing countries

6.2 Methodological Contributions

Validated Measurement Instrument
- Culturally-appropriate AI readiness scale for Indonesian contexts
- Instrument potentially adaptable for other Southeast Asian countries
- Contribution to standardization of AI readiness assessment
Mixed-Methods Approach
- Demonstration of effective integration of quantitative and qualitative methods in technology adoption research
- Model for multi-institutional, multi-regional educational research in developing countries
Baseline Data Establishment
- First comprehensive dataset on AI readiness in Indonesian higher education
- Foundation for future longitudinal and comparative studies
- Benchmark for assessing impact of interventions and policy changes

6.3 Practical Contributions

Policy Recommendations
- Evidence-based recommendations for national education policy on AI integration
- Guidelines for institutional AI adoption strategies
- Input for curriculum development and accreditation standards
Professional Development Framework
- Identification of specific training needs and priorities
- Recommendations for effective faculty development programs
- Strategies for building AI readiness at individual and institutional levels
Implementation Guidelines
- Best practices for AI integration in Indonesian higher education
- Strategies for addressing barriers and leveraging facilitators
- Approaches for equitable AI adoption across diverse contexts
Stakeholder Resources
- Practical tools for institutional self-assessment
- Resources for faculty members to enhance their AI readiness
- Guidelines for administrators and policymakers

6.4 Societal Contributions

Educational Equity
- Understanding of regional and institutional disparities
- Strategies to prevent digital divides in AI adoption
- Promotion of inclusive AI integration
National Competitiveness
- Enhancement of Indonesia's position in global education and technology
- Contribution to human capital development for digital economy
- Support for Indonesia's digital transformation agenda
Regional Leadership
- Positioning Indonesia as a leader in educational AI research in Southeast Asia
- Model for other developing countries facing similar challenges
- Contribution to regional educational development

7. Research Timeline

Year 1: Foundation and Preparation (Months 1-12)

Months 1-3: Literature Review and Framework Development

Comprehensive literature review
Theoretical framework finalization
Research design refinement
Ethics approval applications

Months 4-6: Instrument Development

Questionnaire development
Translation and cultural adaptation
Expert panel review
Cognitive interviews

Months 7-9: Pilot Study

Pilot survey administration (n=150-200)
Pilot data analysis (EFA, reliability)
Instrument refinement
Interview protocol development

Months 10-12: Preparation for Main Study

Institutional partnerships and permissions
Sampling frame finalization
Survey platform setup
Research team training

Year 2: Data Collection and Initial Analysis (Months 13-24)

Months 13-18: Quantitative Data Collection

Main survey launch and administration
Ongoing monitoring and response rate optimization
Data cleaning and preliminary analysis
Validation of measurement model (CFA)

Months 19-24: Qualitative Data Collection

In-depth interviews (n=40-50)
Focus group discussions (n=6-8 groups)
Document collection and analysis
Transcription and initial coding

Year 3: Analysis, Integration, and Synthesis (Months 25-36)

Months 25-27: Advanced Quantitative Analysis

Structural equation modeling
Multi-group analyses
Mediation and moderation testing
Comprehensive statistical reporting

Months 28-30: Qualitative Analysis

Thematic analysis completion
Cross-case analysis
Integration with quantitative findings
Member checking and validation

Months 31-33: Mixed-Methods Integration

Joint display development
Meta-inferences generation
Recommendation development
Stakeholder validation workshops

Months 34-36: Dissertation Writing and Dissemination

Dissertation drafting and revision
Preparation of journal articles
Conference presentations
Policy briefs and stakeholder reports

Year 4 (if needed): Finalization and Defense (Months 37-48)

Months 37-42: Dissertation Finalization

Incorporation of advisor feedback
Final revisions and proofreading
Preparation of defense presentation

Months 43-45: Defense and Revisions

Dissertation defense
Post-defense revisions
Final submission

Months 46-48: Dissemination

Publication of journal articles
Presentation at national and international conferences
Engagement with policymakers and stakeholders
Development of accessible resources for practitioners

8. Budget Considerations

8.1 Major Budget Categories

Personnel Costs:

Research assistants for data collection and transcription
Translation services
Statistical consultation
Transcription services

Data Collection Costs:

Survey platform subscription
Participant incentives
Travel for interviews and focus groups
Accommodation for regional data collection

Equipment and Software:

Statistical software licenses (SPSS, SmartPLS, NVivo)
Recording equipment for interviews
Computer and peripherals

Dissemination Costs:

Conference registration and travel
Journal publication fees (open access)
Printing and binding of dissertation
Stakeholder workshops and events

Contingency: 10-15% of total budget for unforeseen expenses

8.2 Potential Funding Sources

University doctoral research grants
Indonesian Ministry of Education research funding (LPDP, DIKTI)
International research foundations (e.g., Ford Foundation, Asia Foundation)
Technology companies with education initiatives
Collaborative funding with participating institutions

9. Potential Challenges and Mitigation Strategies

9.1 Methodological Challenges

Challenge 1: Achieving Representative Sample

Risk: Low response rates or biased sampling
Mitigation:
- Multiple recruitment strategies
- Institutional partnerships for endorsement
- Incentives for participation
- Extended data collection period

Challenge 2: Measurement Validity in Cross-Cultural Context

Risk: Instruments may not capture Indonesian-specific constructs
Mitigation:
- Rigorous translation and cultural adaptation process
- Extensive pilot testing
- Qualitative phase to capture contextual nuances
- Expert panel with Indonesian educators

Challenge 3: Rapid Technological Change

Risk: AI landscape may evolve during research period
Mitigation:
- Focus on fundamental readiness dimensions rather than specific tools
- Regular literature monitoring
- Flexibility to incorporate emerging developments
- Clear documentation of temporal context

9.2 Practical Challenges

Challenge 4: Geographic Dispersion

Risk: Difficulty accessing remote regions
Mitigation:
- Online data collection methods
- Strategic selection of accessible locations
- Partnerships with local institutions
- Virtual interviews when necessary

Challenge 5: Institutional Access

Risk: Difficulty obtaining permissions from universities
Mitigation:
- Early relationship building with institutions
- Clear communication of benefits
- Flexible participation options
- Institutional anonymity if preferred

Challenge 6: Language and Communication

Risk: Misunderstandings due to language barriers
Mitigation:
- Bilingual research materials
- Native Indonesian speakers on research team
- Professional translation services
- Pilot testing with diverse participants

9.3 Analytical Challenges

Challenge 7: Complexity of Mixed-Methods Integration

Risk: Difficulty meaningfully integrating quantitative and qualitative data
Mitigation:
- Clear integration strategy from design phase
- Use of established integration techniques
- Consultation with mixed-methods experts
- Iterative analysis process

Challenge 8: Handling Missing Data and Non-Response

Risk: Biased results due to systematic non-response
Mitigation:
- Missing data analysis and appropriate techniques (multiple imputation)
- Non-response bias testing
- Sensitivity analyses
- Transparent reporting of limitations

10. Dissemination Plan

10.1 Academic Dissemination

Peer-Reviewed Journal Articles (Target: 4-6 publications)

Validation of AI readiness framework in Indonesian context
- Target: International Journal of Educational Technology in Higher Education
Factors influencing AI readiness: Multi-level analysis
- Target: Computers & Education
Qualitative insights into AI readiness in Indonesian higher education
- Target: Teaching in Higher Education
Mixed-methods study of AI readiness and teaching innovation
- Target: Journal of Computer Assisted Learning
Regional and institutional variations in AI readiness
- Target: Asia Pacific Education Review
Ethical considerations in AI adoption: Indonesian perspectives
- Target: Ethics and Education

Conference Presentations

International Conference on Education and Technology (ICET)
Asian Conference on Education (ACE)
Association for the Advancement of Artificial Intelligence (AAAI) Education Track
Indonesian Computer Science Conference
Regional education technology conferences

10.2 Policy and Practice Dissemination

Policy Briefs

Brief for Indonesian Ministry of Education (Kemendikbudristek)
Briefs for provincial education offices
Policy recommendations for higher education accreditation bodies

Stakeholder Workshops

National workshop with university leaders and policymakers
Regional workshops in major cities
Webinars for broader academic community

Practitioner Resources

AI Readiness Self-Assessment Tool for institutions
Guide for faculty members: "Preparing for AI in Teaching"
Best practices handbook for institutional leaders
Infographics and visual summaries for wide dissemination

10.3 Public Engagement

Media Engagement

Press releases to education media
Op-eds in major newspapers
Interviews on education-focused podcasts and programs

Online Presence

Dedicated research website with resources
Social media dissemination (Twitter, LinkedIn)
Blog posts explaining key findings
Video abstracts of research

Open Access

Open access publication when possible
Sharing of research instruments and protocols
Data sharing (anonymized) when appropriate
Preprints on education repositories

11. Researcher Qualifications and Preparation

11.1 Required Competencies

To successfully conduct this research, the doctoral candidate should develop or possess:

Research Competencies:

Advanced quantitative methods (survey design, SEM, multivariate statistics)
Qualitative methods (interviewing, thematic analysis)
Mixed-methods integration
Large-scale data collection and management

Domain Knowledge:

Understanding of AI technologies and applications in education
Knowledge of Indonesian higher education system
Familiarity with technology adoption theories
Awareness of educational policy contexts

Language and Cultural Competencies:

Fluency in Bahasa Indonesia and English
Cultural sensitivity and understanding of Indonesian academic culture
Ability to navigate diverse institutional contexts

Technical Skills:

Statistical software (SPSS, R, SmartPLS)
Qualitative analysis software (NVivo or ATLAS.ti)
Survey platforms (Qualtrics or similar)
Data visualization tools

11.2 Preparation Activities

Coursework:

Advanced statistics and research methods
Structural equation modeling
Qualitative research methods
Mixed-methods research design
Artificial intelligence in education
Education policy analysis

Preliminary Studies:

Small-scale pilot study on AI readiness
Literature review publication
Methodological paper on measuring AI readiness

Professional Development:

Attendance at relevant conferences
Workshops on SEM and qualitative methods
Networking with AI in education researchers
Engagement with Indonesian education community

12. Conclusion

This doctoral research proposal addresses a critical gap in understanding AI readiness among Indonesian higher education lecturers. As Indonesia pursues digital transformation in education, empirical evidence on educators' preparedness is essential for effective policy development and implementation.

The proposed mixed-methods study will provide the first comprehensive, multi-institutional assessment of AI readiness in Indonesian higher education. By integrating quantitative analysis of readiness levels and influencing factors with qualitative insights into contextual and cultural dimensions, this research will generate both theoretical knowledge and practical recommendations.

The study's significance extends beyond Indonesia. As a large, diverse, and rapidly developing country, Indonesia's experience with AI integration in education can inform understanding of similar processes in other Southeast Asian and developing nations. The validated frameworks and instruments developed through this research will contribute to global scholarship on technology adoption in education.

Ultimately, this research aims to support Indonesian educators, institutional leaders, and policymakers in navigating the challenges and opportunities of AI integration. By providing evidence-based insights and recommendations, the study will contribute to more effective, equitable, and sustainable AI adoption in Indonesian higher education—enhancing teaching quality, student learning, and national competitiveness in the digital age.

References

[1] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

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Appendices

Appendix A: Preliminary AI Readiness Questionnaire (Sample Items)

Appendix B: Interview Protocol Template

Appendix C: Focus Group Discussion Guide

Appendix D: Informed Consent Forms

Appendix E: Timeline Gantt Chart

Appendix F: Preliminary Budget Breakdown

Appendix G: Letters of Support from Institutions

Document Information

Title: Research Proposal - AI Readiness of Teachers and Lecturers in Indonesian Higher Education
Proposed Program: Ph.D. in Computer Science
Date: November 2025
Version: 1.0

This research proposal is designed to be submitted for doctoral program admission and funding applications. It represents a comprehensive plan for investigating AI readiness among Indonesian higher education lecturers and contributing to both theoretical knowledge and practical policy development.