Nov 24, 2024

Public workspaceAI Literacy Framework (ALiF): A Progressive Competency Development Protocol for Higher Education

DOI
dx.doi.org/10.17504/protocols.io.bp2l6dbpzvqe/v1
  • 1Mohammed Bin Rashid University of Medicine and Health Sciences
Nabil Zary
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DOI: dx.doi.org/10.17504/protocols.io.bp2l6dbpzvqe/v1
External link: https://nzary.github.io/alif/
Protocol Citation: Nabil Zary 2024. AI Literacy Framework (ALiF): A Progressive Competency Development Protocol for Higher Education. protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6dbpzvqe/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: In development
We are still developing and optimizing this protocol
Created: November 23, 2024
Last Modified: November 24, 2024
Protocol Integer ID: 112672
Keywords: AI literacy, higher education, competency framework, digital literacy, educational technology
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Abstract
The AI Literacy Framework (ALiF) provides a structured approach to developing AI literacy across three key stakeholder groups in higher education: students, faculty, and staff. This protocol outlines implementing a three-level progression system across four core competency areas: Technical Understanding, Critical Evaluation, Practical Application, and Ethical Considerations. The framework enables institutions to systematically develop AI literacy while maintaining academic integrity and promoting innovation.
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Guidelines
Common Issues and Solutions

Engagement Challenges
  • Solution: Implement targeted communication
  • Alternative: Develop incentive systems
  • Prevention: Regular stakeholder feedback

Resource Limitations
  • Solution: Prioritize critical components
  • Alternative: Phased implementation
  • Prevention: Detailed resource planning

Quality Concerns
  • Solution: Enhanced monitoring
  • Alternative: Peer review systems
  • Prevention: Clear quality standards

Progress Delays
  • Solution: Adjust timelines
  • Alternative: Modified milestones
  • Prevention: Regular progress reviews
Materials
ALiF Framework Documents
  • Complete ALiF framework documentation
  • Implementation guidelines
  • Assessment rubrics
  • Progression pathways documentation
  • Role-specific competency maps


Policy Documents

  • AI use policies
  • Academic integrity guidelines
  • Data protection protocols
  • Ethics guidelines
  • Assessment policies


Training Materials

Foundation Level (L1)

  • Basic AI literacy modules
  • Tool usage guides
  • Assessment guidelines
  • Practice exercises
  • Self-assessment tools

Intermediate Level (L2)

  • Advanced training modules
  • Integration guides
  • Case studies
  • Project templates
  • Peer assessment tools

Advanced Level (L3)

  • Leadership development materials
  • Mentorship guidelines
  • Innovation project guides
  • Strategic planning templates
  • Community building resources


Assessment Tools

Evaluation Materials

  • Competency assessment forms
  • Progress tracking templates
  • Portfolio guidelines
  • Performance indicators
  • Quality assurance checklists

Documentation Templates

  • Implementation tracking forms
  • Progress reports
  • Feedback forms
  • Outcome documentation
  • Impact assessment tools


Support Resources

Technical Resources

  • AI tool guides
  • Platform tutorials
  • Troubleshooting guides
  • Integration manuals
  • Best practice examples

Implementation Support

  • Project management templates
  • Timeline planners
  • Resource allocation guides
  • Risk assessment tools
  • Change management guidelines


Communication Materials

Stakeholder Communications

  • Information packages
  • Presentation templates
  • Progress report formats
  • Newsletter templates
  • FAQ documents

Marketing Materials

  • Program brochures
  • Information posters
  • Digital assets
  • Social media content
  • Event materials


Quality Assurance

Monitoring Tools

  • Quality control checklists
  • Audit templates
  • Review forms
  • Compliance checks
  • Performance metrics

Improvement Resources

  • Feedback collection tools
  • Analysis templates
  • Action plan formats
  • Review guidelines
  • Update protocols


Research and Development

Research Tools

  • Data collection templates
  • Analysis frameworks
  • Survey instruments
  • Interview guides
  • Case study templates

Development Resources

  • Innovation tracking tools
  • Project proposal templates
  • Pilot program guides
  • Evaluation frameworks
  • Impact assessment tools

Before start
Requirements
  1. Institutional commitment to AI literacy development
  2. Basic technological infrastructure
  3. Access to common AI tools and platforms
  4. Designated implementation team
  5. Faculty and staff development resources


Planning & Preparation

  1. Form an implementation committee
  2. Review institutional policies
  3. Assess current AI literacy levels
  4. Identify available resources
  5. Define success metrics
Step 1: Framework Setup
Step 1: Framework Setup
8w
8w
Establish Governance Structure
Create steering committee
Appoint component leads for each competency area
Define reporting structure
Establish communication channels
Define Stakeholder Groups
Identify student cohorts
Map faculty departments
Categorize staff roles
Document specific needs
Resource Assessment
Evaluate available AI tools
Review learning management systems
Assess training capabilities
Identify resource gaps
Step 2: Baseline Assessment
Step 2: Baseline Assessment
4w
4w
Develop Assessment Tools
Create competency evaluation rubrics
Design self-assessment surveys
Develop practical assessment tasks
Establish documentation methods
Conduct Initial Assessments
Administer stakeholder surveys
Conduct practical evaluations
Document current practices
Identify development needs
Analysis and Planning
Review assessment results
Identify priority areas
Create development roadmap
Set measurable targets
Step 3: Implementation Strategy
Step 3: Implementation Strategy
12w
12w
Level 1 Implementation
Deploy foundation training
Establish basic guidelines
Implement monitoring systems
Provide support resources
Level 2 Development
Create advanced modules
Develop practical exercises
Implement peer learning
Track progression
Level 3 Leadership
Identify potential leaders
Create mentorship programs
Develop innovation projects
Establish communities of practice
Step 4: Assessment and Progression
Step 4: Assessment and Progression
Progress Monitoring
Regular assessments
Portfolio reviews
Practical evaluations
Feedback collection
Quality Assurance
Review implementation
Validate outcomes
Adjust procedures
Document best practices
Continuous Improvement
Gather feedback
Update materials
Enhance processes
Share outcomes
Optional Steps
Optional Steps
Enhanced Implementation
Cross-institutional collaboration
Research integration
Industry partnerships
Innovation projects
Extended Assessment
External validation
Research studies
Impact analysis
Longitudinal tracking
Protocol references
Theoretical Foundations and AI Literacy
  1. Holmes, W., Bialik, M., & Fadel, C. (2019). "Artificial Intelligence in Education: Promises and Implications for Teaching and Learning." Center for Curriculum Redesign.
  2. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). "Systematic review of research on artificial intelligence applications in higher education." International Journal of Educational Technology in Higher Education, 16(1).
  3. Long, D., & Magerko, B. (2020). "What is AI Literacy? Competencies and Design Considerations." CHI Conference on Human Factors in Computing Systems.
  4. Roll, I., & Wylie, R. (2016). "Available, Accessible, Aware, and Able: Understanding the Four A's of AI Literacy." Journal of Learning Analytics, 3(2), 152-164.

Competency Frameworks and Assessment

  1. Carretero, S., Vuorikari, R., & Punie, Y. (2017). "DigComp 2.1: The Digital Competence Framework for Citizens." Publications Office of the European Union.
  2. UNESCO. (2021). "AI and Education: Guidance for Policy-makers." UNESCO Digital Library.
  3. EDUCAUSE. (2022). "2022 EDUCAUSE Horizon Report, Teaching and Learning Edition." EDUCAUSE Publications.
  4. World Economic Forum. (2022). "Building an AI Powered Organization." World Economic Forum White Paper.


Implementation and Practice

  1. Picciano, A. G. (2019). "Artificial Intelligence and the Academy's Loss of Purpose." Online Learning, 23(3), 270-284.
  2. Hwang, G. J., & Chen, C. L. (2022). "Artificial Intelligence in Education: A Review." Computers and Education: Artificial Intelligence, 3, 100082.
  3. Luckin, R., Holmes, W., & Forcier, L. K. (2016). "Intelligence Unleashed: An argument for AI in Education." Pearson.
  4. Guan, C., Mou, J., & Jiang, Z. (2020). "Artificial intelligence innovation in education: A twenty-year data-driven historical analysis." International Journal of Innovation Studies, 4(4), 134-147.


Ethics and Policy

  1. Jobin, A., Ienca, M., & Vayena, E. (2019). "The global landscape of AI ethics guidelines." Nature Machine Intelligence, 1(9), 389-399.
  2. Prinsloo, P., & Slade, S. (2017). "Ethics and Learning Analytics: Charting the (Un)Charted." In Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.), Handbook of Learning Analytics (pp. 49-57).
  3. Zawacki-Richter, O., et al. (2022). "Ethics of Artificial Intelligence in Education." International Review of Research in Open and Distributed Learning, 23(1), 138-159.


Academic Integrity and AI

  1. Eaton, S. E., & Turner, K. L. (2020). "Exploring academic integrity and mental health during COVID-19: Rapid review." Journal of Contemporary Education Theory & Research, 4(2), 35-41.
  2. Mindzak, M., & Eaton, S. E. (2021). "Artificial Intelligence and Academic Integrity: New Educational Challenges for Canadian Higher Education." Canadian Journal of Educational Administration and Policy.


Institutional Implementation

  1. García-Peñalvo, F. J., et al. (2022). "Artificial Intelligence in Higher Education: A Systematic Mapping." Sustainability, 14(3), 1493.
  2. Tsai, Y. S., et al. (2021). "SHEILA framework: informing institutional strategies and policy processes of learning analytics." Journal of Learning Analytics, 8(3), 31-59.


Assessment and Evaluation

  1. Hernández‐Leo, D., et al. (2019). "Analytics for learning design: A layered framework and tools." British Journal of Educational Technology, 50(1), 139-152.


Recent Reviews and Meta-Analyses

  1. Du, X., et al. (2021). "Artificial intelligence in education: A systematic review from 2011 to 2020." Education and Information Technologies.
  2. Zawacki-Richter, O., et al. (2020). "Systematic review of research on artificial intelligence applications in higher education – where are the educators?" International Journal of Educational Technology in Higher Education, 17(1).


Professional Development

  1. Mishra, P., & Koehler, M. J. (2006). "Technological pedagogical content knowledge: A framework for teacher knowledge." Teachers College Record, 108(6), 1017-1054.
  2. Trust, T., & Whalen, J. (2020). "Should Teachers be Trained in Emergency Remote Teaching? Lessons Learned from the COVID-19 Pandemic." Journal of Technology and Teacher Education, 28(2), 189-199.
Acknowledgements
My colleagues at the Institute of Learning for sharing their experience in implementing change.