Initiative to Enhance University Courses through the Optimal Integration of Artificial Intelligence

In line with the University's strategic directions and objectives, and in response to the rapid transformation of higher education driven by the digital revolution and the growing use of artificial intelligence (AI), the University has launched the Initiative to Enhance University Courses through the Optimal Integration of Artificial Intelligence.

The initiative builds on the University's adoption of a policy governing the use of generative AI and seeks to rethink how AI is incorporated into academic courses. It focuses on enhancing teaching and assessment practices, equipping students with future-ready skills, and promoting the effective, responsible, and purposeful use of AI while upholding academic integrity and quality standards. Ultimately, the initiative aims to enhance the quality of teaching and learning and foster excellence and innovation.

Initiative Objectives

1. Review and enhance university courses to support the effective integration of AI technologies.
2. Develop students' higher-order skills in critical thinking, analysis, innovation, and decision-making.
3. Equip faculty members with the knowledge and skills to effectively integrate AI tools into teaching and learning.
4. Promote the responsible and well-governed use of AI while ensuring academic integrity and high-quality learning outcomes.

Strategic Objective Linked to the Initiative

• Developing competitive competencies locally and internationally.

Initiative Areas

Course Content (Activities and Projects):
Enrich course content with intelligent learning resources, and design activities and projects where AI serves a clear purpose and adds value, such as data analysis, simulations, and initial content generation.

Teaching Strategies:
Adopt project-based and inquiry-based learning approaches to promote active learning and develop students' skills.

Assessment Methods:
Redesign assessment approaches to measure higher-order skills, such as evaluating AI-generated outputs and developing innovative solutions based on them. This includes restructuring assessments in light of AI by identifying tasks where AI use is permitted and those requiring independent performance, while assessing students' thinking processes, verification, critical evaluation, and decision-making rationale alongside the final product.

Implementation Phases

Phase One: Preparation

Objective:
Officially launch the initiative and establish a unified vision and implementation plan across colleges.

Key Activities:

• Issue the official decision to form the initiative's executive committee at the University level.
• Hold the first introductory meeting for initiative coordinators across colleges.
• Develop initiative guidelines and standardized templates (e.g., course selection form, documentation form, and evaluation form).
• Conduct an introductory workshop on integrating AI into teaching and assessment, supported by a short practical implementation track throughout the course development process. The track will be based on the TPACK framework, which integrates technological, pedagogical, and content knowledge, while providing consultations and design sessions for participants as needed.
  
  Outputs:

• An approved implementation plan.
• Formation of supervisory committees within colleges.
• An initial database of college-level initiative coordinators.

Phase Two: Planning

Objective:
Identify priority courses and develop plans for integrating AI into them.

Key Activities:

• Select up to two courses from each program based on educational priorities and established selection criteria.
• Develop an AI integration plan for each course, starting with the intended learning outcomes, followed by the educational purpose and added value of AI, the level of integration, its pedagogical rationale, and the assessment approach. The focus should be on the function of the technology rather than the specific tool, given the rapid evolution of AI tools.
• Submit the completed templates to the Executive Committee for approval.

Outputs:

• A list of courses identified for development in each college, with a review of their learning outcomes as an essential step for all selected courses. This review will consider the extent to which AI can perform some of the cognitive tasks traditionally assessed through conventional assignments, without necessarily requiring changes to the learning outcomes.
• Initial course development plans approved by the Executive Committee.

Phase Three: Implementation

Objective:
Integrate AI technologies into selected courses and monitor implementation progress.

Key Activities:

• Revise course activities, teaching strategies, assessment methods, and, where necessary, learning outcomes to incorporate AI, with the level of integration not exceeding 20%. The percentage will be calculated using a clearly defined measure based on one or more relevant dimensions, such as content, activities, learning time, or assessment weighting. Beyond this quantitative measure, the depth and educational value of AI integration will be described using the SAMR model, which distinguishes between substitution, augmentation, modification, and redefinition according to the nature of the course and its intended learning outcomes.
• Implement approved activities and projects that incorporate AI tools, such as image-generation tools and intelligent text-analysis applications.
• Hold regular follow-up meetings with college coordinators to monitor progress, address challenges, and gather feedback.

Outputs:

• Documentation of the enhanced courses, highlighting the changes made to course activities, teaching strategies, assessment methods, and/or learning outcomes, providing a qualitative account of the outcomes of these enhancements.
• An interim report from each college outlining implementation progress and providing examples of the enhanced activities.

Phase Four: Evaluation and Final Reporting

Objective:
Evaluate the initiative's outcomes and assess its impact on course quality and learning outcomes.

Key Activities:

• Receive the final reports from the colleges.
• Analyze the data and measure the initiative's performance indicators across three levels: implementation indicators, such as the number of participating colleges, courses, and students; experience quality indicators, such as student and faculty satisfaction; and educational outcome indicators linked to the targeted learning outcomes and skills. The phase will also include a direct assessment of the initiative's impact on learning outcomes in a sample of courses.
• Hold the final meeting of the University Executive Committee to review achievements and recommendations.
• Prepare the University's final report and submit it to the Vice Rectorate for Academic Affairs.

Outputs:

• A comprehensive final report on the initiative at the University level.
• A list of enhanced courses and best practices.
• Recommendations for scaling the initiative in the next phase, drawing systematically on the experience of the first phase by documenting best practices, challenges, lessons learned, and successful activities and assessment approaches. This will transform the experience into transferable institutional knowledge that can be applied across other colleges and programs, rather than simply expanding the initiative in terms of numbers.

Initiative Outcomes

• The initiative achieved positive results in terms of implementation and stakeholder satisfaction. It involved six academic colleges across diverse disciplines, resulting in the enhancement of 54 courses and directly benefiting more than 3,754 students, with faculty members contributing to the course development process.
• Overall satisfaction averaged 4.16 out of 5 among students and 4.21 out of 5 among faculty members, indicating a high level of stakeholder satisfaction and positive acceptance of the initiative during its initial implementation. These results support the initiative's potential for expansion and the launch of its second phase to include the remaining colleges and programs. Direct measurement of its impact on learning outcomes and targeted skills will remain a priority for the next phase, providing stronger evidence of the initiative's educational effectiveness and the sustainability of its outcomes.​
  

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