Instructional Design Model

The chosen instructional design framework for the minicourse “Ethical Integration of AI Tools for Instructors’ Professional Development” is a hybrid approach that blends the Successive Approximation Model (SAM) (Allen, 2012) with key components of ADDIE (Branch, 2009). This selection was made after thorough consideration of the minicourse’s learning goals, content complexity, learner characteristics, available resources, technological tools, and Evaluation criteria (University of Maryland Global Campus, n.d.).

Learning Objectives and Content Complexity.
The minicourse instructs instructors to understand key ethical concepts such as bias, transparency, and data privacy, and to incorporate decision-making frameworks into real teaching scenarios. While short lectures or multimedia are effective for introducing these ideas, the course also highlights scenario analysis and reflective practice. Critical thinking and real-world application may not be enough for a quick design process; SAM thereby facilitates iterative development of case studies and simulations, while ADDIE aligns learning objectives, activities, and measurable outcomes. For example, an instructor's ability to identify, evaluate, analyze, design, and apply principles, tools, and scenarios is crucial for ethically integrating AI tools into their professional practice.

Learner Profile.
The target audience, higher education instructors, are self-motivated adult learners who prioritize flexibility, relevance, and real-world applicability. SAM’s collaborative, learner-focused method enables the early development of tools such as AI-use ethical checklists, which can be improved through instructor feedback. However, adult learners in professional development also value clarity and structure. Including the systematic analysis phase of ADDIE ensures the course addresses actual performance gaps and caters to instructors with varying levels of AI knowledge.

Available Resources and Technology.
Because this minicourse may rely on free or low-cost digital tools and limited development resources, SAM’s streamlined, iterative cycles enable the efficient creation of learning objects with minimal documentation. At the same time, technological requirements, such as multimedia integration or scenario-based activities, must remain feasible within the current platform's capabilities. The selected hybrid model provides flexibility while accounting for technical and budgetary limitations. For example, the mini course may rely on subject-matter experts, technological resources such as AI tools and learning management systems (LMS), and institutional support to align with available resources.

Evaluation and Feedback.
Evaluation plays a crucial role due to the ethical and policy-sensitive aspects of AI integration. SAM encourages continuous formative feedback through iterative updates (Allen, 2012), making it well-suited for refining content during development. Meanwhile, ADDIE’s focus on formal evaluation guarantees that learning outcomes are assessed and aligned with institutional standards. Using both formative and summative assessments, such as scenario analyses and participant surveys, enhances instructional quality and accountability.

In summary, the hybrid SAM-ADDIE model is ideal for this minicourse as it combines flexibility with structure. It addresses the challenges of ethical AI integration, meets the needs of adult professional learners, accounts for resource constraints, and promotes continuous improvement alongside rigorous standards evaluation.