The Intersection of Artificial Intelligence and Instructional Design Practice: A Systematic Review

Synthesis: This systematic review (PRISMA 2020) of 28 peer-reviewed articles (2020–2025) examines how artificial intelligence is being used within instructional design (ID) processes. The field is still emerging: most studies explore perceptions and reflections of designers and teachers, followed by evaluative studies and a small set of tool-development efforts. Across studies, AI is most frequently used to enhance or assist instructional planning, support teaching delivery, and design Assessment; its clearest benefit is offloading administrative and repetitive tasks (grading, feedback, progress monitoring) that free designers for higher-order pedagogical decision-making. Central challenges are pedagogical alignment, practitioner readiness, trust and reliability, and content reliability/transparency. Overall, AI is conceptualized not as a mere tool but as a co-worker, collaborator, and design partner—an augmentative rather than replacement technology requiring human oversight.

Core Finding

AI is integrated into instructional design as a collaborative design partner rather than an autonomous agent, functioning across the analysis, design, and development phases of models like ADDIE, while remaining less visible in implementation and evaluation. Its value is role-sensitive: it augments human judgment, pedagogical reasoning, and decision-making, but pedagogically critical decisions must remain anchored in human expertise and oversight.

How AI Is Used in Instructional Design

Across the reviewed studies, AI serves several distinct purposes in the ID process:

• Enhancing or assisting instructional design (22 studies, 78.5%): AI supports pre-instructional planning, analysis, and decision-making—conceptualizing, structuring, and aligning instruction with learning objectives and learner needs, contributing to design cognition rather than media production.
• Supporting teaching delivery (17 studies, 60.7%): AI generates explanations, discussion questions, activity ideas, lesson scenarios, and reflection questions, and adapts language complexity to different learner groups—mediating teacher–learner interaction rather than shaping ID decisions.
• Assessment design (14 studies, 50%): AI creates quizzes, question banks, and assessment criteria, and provides instant or adaptive Feedback, supporting continuous assessment and the evaluation and revision of assessment strategies.
• Innovating in instructional strategies (9 studies): AI enables intelligent tutoring systems, adaptive learning environments, learning analytics, and predictive instruction that reshape instructional models.
• Multimedia content creation (7 studies) and facilitating accessibility and inclusivity (4 studies): AI produces image/audio/video learning materials and reduces access barriers through transcripts and narrated descriptions of visual data.

Reported Benefits

• Assisting with administrative and repetitive tasks (18 studies, 64.2%) is the most significant benefit—automating grading, feedback generation, progress monitoring, and knowledge retrieval, and reallocating human effort to higher-level ID and pedagogical decision-making.
• Inspiration and idea generation (11 studies, 39.3%): AI stimulates new instructional ideas, enhances creativity, and supports engaging teaching approaches in early instructional planning.
• Facilitating teacher professional growth (8 studies, 28.5%): AI supports reflective practice—feedback on instructional practices, prompt refinement, interpreting classroom observations, and self-evaluation of teaching effectiveness.
• Data-driven decisions (7 studies, 25%): AI analyzes learner data, evaluates instructional interventions, and generates improvement recommendations, enhancing analytical insight and decision quality.
• Academic language and communication development (3 studies): AI supports grammatical precision, coherence, and structured linguistic feedback.

Challenges and Limitations

Challenges most frequently cited are pedagogical alignment (14 studies), teacher/designer readiness rooted in limited AI literacy and prompt uncertainty (12 studies), trust and reliability concerns (11 studies), and prompting iteration workload (10 studies). Additional challenges include ethical uncertainty around plagiarism and academic integrity (8 studies), organizational barriers (7 studies), contextual adaptation difficulties (5 studies), and learner engagement concerns (5 studies).

Limitations center on content reliability and transparency (16 studies)—inaccurate, fabricated, or contradictory outputs, with GenAI characterized as a "black box"—followed by technical/tool-specific limits (12 studies), contextual and cultural blindness (10 studies), low cognitive demand outputs (7 studies), phase-limited integration (5 studies), and lack of deep pedagogical reasoning (3 studies). These findings collectively affirm a human-in-the-loop view: GenAI functions as an augmentative rather than a replacement technology, with human expertise essential for interpreting, refining, and contextualizing machine-generated content.

Relevance to the wiki

This paper is a cornerstone contribution to the wiki's two most emphasized concepts—Instructional Design and teacher-AI collaboration. It moves beyond tool-centered accounts to provide a process-level, role-sensitive synthesis of how AI reshapes instructional design work, and it foregrounds the evolving roles of instructional designers and teachers as AI becomes a collaborator rather than a tool. Its treatment of pedagogical alignment, human oversight, and practitioner readiness connects directly to questions of AI literacy, professional development, and the conditions under which AI genuinely enhances (rather than erodes) design quality. It also consolidates evidence on Feedback, assessment design, and personalization within a design-process frame.

Connected Concepts

• Instructional Design
• Generative AI
• Human AI Collaboration
• Human In The Loop AI
• Teacher Role
• Pedagogy
• Feedback
• Assessment
• Personalized Learning
• professional development
• LLM
• AI Education
• Ethics

Connected Articles

• Teacher AI Teaming Five Levels
• AI Distance Education Systematic Review 2026

Citation

Kibar, P. N., & Ilgaz, H. (2026). The intersection of artificial intelligence and instructional design practice: A systematic review. Educational Technology Research and Development.