Information Package / Course Catalogue
Artificial Intelligence Applications in Tourism
Course Code: THM150
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

The aim of this course is to equip students in tourism and hospitality programs with the operational and technological competence to apply artificial intelligence—and especially generative AI tools—responsibly, ethically, and effectively in their own professional fields. Through real-world professional scenario applications using generative AI tools such as ChatGPT, Gemini, and Claude and image-generation platforms, prompt-engineering workshops, industry guest experiences, and integrated project work, students are prepared for internships and working life not as mere consumers of AI, but as users who can produce solutions for the sector's real needs.

Course Content

This course aims to equip tourism and hospitality students with the competence to turn artificial intelligence tools into professional applications. Students will learn the fundamental concepts of AI (narrow/general AI, machine learning, generative AI, agentic AI), the principles of prompt engineering, text and visual content generation with generative AI, guest/customer communication and sector workflows, and the responsible use of AI outputs in terms of accuracy, copyright, bias, and personal data protection (KVKK). Applied work is carried out through real scenarios specific to the hotel, travel, and culinary fields. The course also includes industry guest sessions and the design and presentation of an AI-supported mini application/workflow project at the end of the term.

Name of Lecturer(s)
Learning Outcomes
1.Defines and distinguishes between the fundamental concepts of artificial intelligence (narrow/general AI, machine learning, generative AI, agent-based AI).
2.Explains the application areas of AI in the tourism and hospitality sector (recommendation systems, dynamic pricing, chatbot, text/image generation).
3.Creates effective prompts for a professional task using generative AI tools.
4.Evaluates AI outputs in terms of accuracy, copyright, bias, and personal data (KVKK) and applies responsible use principles.
5.Generates AI-supported text, image, and workflow outputs in a scenario specific to their professional field (hotel/travel/kitchen).
6.Designs, presents, and critically evaluates the process of an AI-supported application project.
Recommended or Required Reading
1.Taşbağ, F., Alpaslanoğlu, A., Armutçu, B., & İnce, E. (2024). Turizmde akıllı çözümler: Yapay zekâ uygulamaları. Ekin Yayınevi.
2.Karaca, Ş. (ed.) (2024). Turizm ve Dijital Pazarlama (Teori ve Uygulamalar). Özgür Yayınları. DOI:
Weekly Detailed Course Contents
Week 1 - Theoretical
The historical process of technological transformation in tourism and the concepts of Tourism 4.0/5.0; the difference between digitalization and smartification; core components of the digital ecosystem (hardware, software, data, cloud, mobile); the place and importance of AI within this transformation; the impact of AI on guest experience, operational efficiency, and revenue management; course operation and project introduction.
Week 2 - Theoretical
The concept and working logic of the Internet of Things (IoT); smart room technologies (sensor thermostats, smart locks, voice-controlled assistants); RFID/NFC wristband and card applications; location-based guest services; hotel and resort examples (Hilton Connected Room, Marriott IoT Hotel); the relationship between IoT and AI (data generation › smart decisions); energy management, sustainability, and data security/privacy.
Week 3 - Theoretical
Definition, historical development, and fundamental concepts of AI; the distinction between narrow AI (ANI), general AI (AGI), and super AI (ASI); the relationship between machine learning, deep learning, and natural language processing (NLP); supervised, unsupervised, and reinforcement learning explained with simple examples; reflections of AI in daily life (recommendation systems, facial recognition, voice assistants); main areas of AI use in tourism (forecasting, personalization, automation, content generation).
Week 4 - Theoretical
The concept of Generative AI and its difference from traditional AI; the working logic of Large Language Models (token, probability, context window); comparison of leading tools: ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), Copilot (Microsoft); introduction to tools that generate text, images (DALL·E, Midjourney), audio (ElevenLabs), and video (Sora, Runway); the concept of hallucination and verifiability; task-based comparison of the models' strengths and weaknesses.
Week 5 - Theoretical
The concept and importance of prompt engineering and the core components of an effective prompt (role, context, task, format, constraint); zero-shot, few-shot, and chain-of-thought techniques; persona-based prompt writing; the iterative improvement loop; breaking down complex tasks via prompt chaining; "good" and "bad" prompt examples in tourism scenarios; critical evaluation of outputs.
Week 6 - Theoretical
The main application areas of artificial intelligence in tourism businesses are discussed. Personalized recommendations, pricing and demand forecasting, chatbots and virtual assistants, digital marketing content, voice assistants, smart hotel and smart destination applications are examined at a general level. In addition, the applicability of automation, robotic systems, drones, and VR/AR technologies in tourism is evaluated.
Week 7 - Theoretical
The principles of responsible and ethical use of artificial intelligence are discussed. Accuracy, hallucination, verification, copyright, intellectual property, bias, fairness, personal data protection, and academic integrity are evaluated in the context of tourism. The importance of human oversight is emphasized, and a general review of the first six weeks is conducted.
Week 8 - Intermediate Exam
Midterm Exam: General review of the theoretical topics covered in the first seven weeks (fundamentals of artificial intelligence, generative AI, requests engineering, and responsible/ethical use).
Week 9 - Practice
Request Engineering Workshop: Introduction to professional AI tools and basic use; practical work on writing effective requests; generating and developing them on replicated devices. Applied workshop with Generative AI.
Week 10 - Practice
Content and Communication with AI: Production of written content and customer/guest communication with AI support; preparation of information, response, and promotional texts; language and style management. Applied content work.
Week 11 - Practice
Visual and Promotional Content with AI: Presentation principles; review of produced content; visual and promotional content created with AI tools. Applied visual work.
Week 12 - Practice
Analysis and Evaluation with AI: Use of AI feedback and data analysis; simple reporting and summarizing; interpretation of results. Applied Analysis Study
Week 13 - Practice
Workflows and Automation with Artificial Intelligence: facilitating repetitive tasks with artificial networks; basic work environment and automation logic; applied examples. Applied work performance study
Week 14 - Practice
Project Development Workshop: enhances what has been learned throughout the semester by combining it; mentored work, feedback and presentation preparation. Project development and basic application
Week 15 - Final Exam
End-of-Term Exam (Final): presentation of student projects and general evaluation of the topics covered throughout the semester. Project presentations
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment2%20
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140114
Assignment26114
Midterm Examination110010
Final Examination110010
TOTAL WORKLOAD (hours)76
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
OÇ-1
4
5
4
4
3
2
1
OÇ-2
4
5
4
4
3
2
1
OÇ-3
4
5
4
4
3
2
1
OÇ-4
4
5
4
4
3
2
1
OÇ-5
4
5
4
4
3
2
1
OÇ-6
4
5
4
4
3
2
1
Adnan Menderes University - Information Package / Course Catalogue
2026