Information Package / Course Catalogue
Ai Applications in Video Editing
Course Code: RTS317
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 1
Prt.: 2
Credit: 2
Lab: 0
ECTS: 5
Objectives of the Course

The objective of this course is to introduce students to current artificial intelligence (AI) technologies, tools, and algorithms used in video and audio editing processes within cinema, television, and digital broadcasting. The course aims to enable students to optimize editing workflows through AI applications, combine creative editing techniques with innovative technologies, and manage these processes from a critical perspective within the framework of national/international ethical principles and copyright laws.

Course Content

This course covers the theoretical and practical dimensions of artificial intelligence technologies that are transforming post-production processes in the cinema, television, and new media industries. The curriculum includes the analysis of AI-powered editing software, automated video and audio synchronization, intelligent color grading, audio cleaning, the use of AI in visual effects (VFX), and contemporary techniques such as generative AI and deep editing. Furthermore, the speed, creative possibilities, and sectoral labor transformation brought by AI in editing, along with copyright and ethical issues, are discussed from a critical perspective, and students develop hands-on applied projects.

Name of Lecturer(s)
Learning Outcomes
1.Integrate contemporary AI-powered editing and audio software into professional post-production workflows.
2.Manage time and resources efficiently using AI automations like text-based editing, scene detection, and smart media management.
3.Develop original audio, music, visual effects (VFX), and frame expansions (Outpainting) using generative AI tools.
4.Blend AI-driven efficiency with core cinematic rules of editing, such as pacing, rhythm, and emotional resonance.
5.Critically evaluate copyright issues, ownership, and deepfake/disinformation risks to design legally sound projects.
Recommended or Required Reading
1.Hutson, J., & Smith, A. (2024). Cinematic algorithms: The rise of generative AI in video art and visual culture. Routledge.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
The evolution of AI in post-production and its differences from traditional editing; interface and capability analysis of core AI tools such as Adobe Premiere, DaVinci Resolve, and Runway.
Week 2 - Theoretical & Practice
The relationship between big data and editing, AI-driven auto-tagging, and facial recognition; automatic multi-cam audio/video synchronization and classification of raw footage.
Week 3 - Theoretical & Practice
Speech-to-Text algorithms and time management; editing video via transcripts like editing a text document and automated removal of filler words.
Week 4 - Theoretical & Practice
Video content analysis and Scene Edit Detection algorithms; splitting long raw footage into scenes with AI and utilizing Auto Reframe for social media formats.
Week 5 - Theoretical & Practice
Audio waves, noise profiles, and frequency isolation; enhancing poorly recorded dialogue to studio quality using tools like Adobe Podcast or Izotope RX.
Week 6 - Theoretical & Practice
Synthetic voice generation, voice cloning, and voiceover usage; voice cloning practices with ElevenLabs, and generating AI-driven background music and sound effects for timelines.
Week 7 - Theoretical & Practice
Color theory, the logic of LUTs, and AI color analysis; automatically matching colors across different cameras using AI and executing fast cinematic color grading.
Week 8 - Theoretical & Practice
Final project briefing and AI editing workflow strategies; ideation, scripting, material preparation, and pitch-deck presentations for individual/group editing projects.
Week 9 - Theoretical & Practice
Computer vision, rotoscoping principles, and layering techniques; automatically removing unwanted objects from video (Inpainting) and smart masking without green screens.
Week 10 - Theoretical & Practice
Integrating Generative AI into video timelines; restoring low-quality archival footage with AI (Upscaling) and extending video frames creatively (Outpainting).
Week 11 - Theoretical & Practice
Principles of GANs, face-swapping, and lip-sync technologies; analyzing deepfakes for educational purposes and utilizing automated lip-sync tools for multilingual projects.
Week 12 - Theoretical & Practice
Copyright issues in AI editing, ownership rights, disinformation risks, and the future of the profession; case studies and designing legally compliant, copyright-safe AI workflows.
Week 13 - Theoretical & Practice
Balancing rhythm, pacing, and emotion in editing with automated AI efficiency; optimizing and revising final editing projects in a laboratory environment under instructor guidance
Week 14 - Theoretical & Practice
Technical and aesthetic critical analysis of the completed student works; screening, presentation, and grading of final projects (short films, documentaries, or PSAs) created using AI tools.
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures5%5
Practice Examination1%15
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141128
Lecture - Practice140228
Practice Examination115015
Midterm Examination120121
Final Examination130131
TOTAL WORKLOAD (hours)123
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
PÇ-15
PÇ-16
OÇ-1
4
5
5
5
OÇ-2
5
5
5
5
3
OÇ-3
5
3
5
4
5
OÇ-4
5
5
5
OÇ-5
5
4
4
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026