For my final project in the Canvas Certified Educator (CCE) program, I wanted to bring together two worlds I know well: the close reading of cinema and the thoughtful integration of AI in learning design. The result is a Canvas module for film studies students that teaches scene analysis with AI support—balancing technological assistance with human interpretation.

This project is more than just a course outline. It’s a demonstration of how instructional design principles, grounded in theory and driven by current trends, can enhance creative disciplines without diluting their artistry.

---

Why Scene Analysis and AI?

Scene analysis is at the heart of film studies. It’s where students learn to break a film down into its technical and narrative components—examining shot composition, camera movement, editing rhythm, lighting, and sound—to uncover meaning.

But it’s not easy. Managing multiple visual and thematic threads at once can overwhelm even advanced students. This is where AI becomes a partner: generating shot lists, identifying visual motifs, and annotating scenes so students can focus on the higher-order task of interpretation.

---

Grounding the Design in ADDIE

The design process followed the ADDIE Model, providing a clear framework from concept to evaluation:

• Analysis: Identify learner needs. Many students have the vocabulary of cinema but struggle to connect technical choices to thematic meaning. Pre-assessments and surveys help gauge both film knowledge and comfort with AI tools.
• Design: Structure learning from foundational scene analysis concepts to complex, AI-assisted interpretation. Blend multimedia materials to suit different learning styles and ensure accessibility.
• Development: Build interactive lectures, guided examples, AI tool demos, and scaffolded assignments. Everything includes closed captions, transcripts, and clear navigation.
• Implementation: Deliver via Canvas, combining asynchronous content with optional synchronous discussions. AI tools are integrated seamlessly through embedded links and demonstrations.
• Evaluation: Use formative checks (quizzes, discussions) and a summative final project to measure outcomes. Gather feedback on the role of AI in the learning process.

---

Theoretical Foundations

This learning experience is grounded in Constructivism and supported by Cognitivism.

• Constructivism: Students build knowledge by applying scene analysis frameworks to authentic film clips, engaging in peer discussions, and reflecting on their process. AI acts as a collaborator—providing annotations and patterns that students evaluate rather than accept blindly.
• Cognitivism: Scene analysis requires managing complex information simultaneously. The module scaffolds tasks, starting with single elements like camera movement before layering in others. AI reduces extraneous cognitive load by handling repetitive technical breakdowns.

---

Learning Goals

By the end of the module, students will be able to:

• Identify and describe key cinematic techniques, from shot composition to sound design.
• Apply analytical frameworks to interpret narrative and thematic meaning.
• Use AI tools to generate and evaluate annotations, shot lists, and thematic patterns.
• Compare AI-generated and human-led analyses for accuracy and depth.
• Provide structured, rubric-based feedback on peer work.
• Reflect critically on AI’s role in creative and analytical practice.

---

Trends and Design Features

This module integrates three key instructional design trends:

1. Blended Learning: Asynchronous modules (lectures, guided examples, AI tutorials) prepare students for richer live discussions.
2. Gamification: “Scene analysis challenges” award badges for milestones like identifying a director’s signature technique or producing the most original thematic interpretation.
3. AI & Machine Learning: AI summarizes peer discussions and surfaces recurring ideas, helping students see the evolving “story” of the class’s interpretations.

---

Activities in Action

• Interactive Lecture: A short video followed by an instructor-led breakdown of a film clip.
• AI Tool Practice: Students use AI to annotate a scene and identify patterns in composition or theme.
• Focused Analysis Assignment: Apply a framework like mise-en-scène and compare personal findings with AI output.
• Peer Review: Exchange annotated clips and give feedback using a rubric.
• Scene Analysis Challenge: Compete to produce the most compelling thematic interpretation of a scene, blending AI and human insights.
• Final Project: Deliver a comprehensive scene analysis that combines AI data with personal interpretation, plus a reflection on AI’s impact.

---

Why This Matters

This project shows that instructional design is not limited to traditional academic or workplace learning. By applying established ID processes and theories to film studies, it bridges creative practice with emerging technologies—without losing sight of the artistry at the core of cinema.

It’s a model for how educators can incorporate AI to enhance learning while preserving authenticity, inviting students to become both skilled analysts and critical users of technology.