Module Description:

The module is centered on the impact of Artificial Intelligence (AI) and data analytics on the tourism sector. It aims to make certain students appreciate the impact of certain AI technologies such as machine learning, natural language processing, and predictive analytics on the decision-making, and customer service and business operations in the tourism industry. Some topics to be covered under the module are: in data gathering and processing, marketing using AI, customer behaviour biometry, as well as forecasting and offering specialised travel services. It is expected that students will be trained in the theory and practice of data and AI technologies so as to transform and enhance the competitiveness of tourism enterprises. Other topics to be covered are the ethical and social issues of AI in tourism (125 Hours).

Learning Outcomes:

Competences:

At the end of the module/unit the learner will have acquired the responsibility and autonomy to: 

  • Application of AI and data analytics in the tourism industry and its particular use cases.
  • Decision making in tourism industry requires one to have analytical skills.
  • Proficiency of AI-driven systems for customer segmentation and targeting.
  • Application of AI for predictive analytics in forecasting tourism demand.
  • Reporting to stakeholders involves data visualization as well as presenting relevant insights and conclusions.
  • AI and data techniques for tourism raise ethical issues that need to be addressed.
  • AI components need to be strategically incorporated into business models of the tourism industry.
  • Identifying trends and patterns in tourism requires proficiency in AI and data-driven technologies.
  • Study and appraisal of AI and data-driven technologies in tourism reveals their consequences and impact.

Knowledge:

At the end of the unit the learner will have been exposed to the following: 

  • The importance of modern AI technologies and big data in the tourism industry, in particular, for modern business.
  • Understanding of the key critical analytics methods and methodologies.
  • Comprehension of the functions of AI technologies in tourism, for example, Natural Language Processing and recommenders.
  • Understanding of the tourism system and its related data anomalies and challenges.
  • Comprehension of data and information sources, as well as methods of data collection concerning the tourism industry.
  • The ethical, privacy, and security concerns of AI and data uses.
  • Understanding of models for forecasting tourism and travel demand, including their uses.
  • User activity in relation to tourism advertisement and marketing and its functions.
  • Applications and technologies for tourism analytics driven by AI.

Skills:

The following abilities will have been attained by the student by the end of the unit: 

  • The ability to select, organise, and analyse tourism data in order to extract useful insights and conclusions.
  • AI technologies and their application tools, including TensorFlow, Python, and R, and their relevance in tourism domain.
  • Formulating advanced tourism analytical models to predict developments in allied sectors and the ever-changing landscape of tourism.
  • Creating insightful visualizations to portray tourism datasets interpretively and analytically.
  • Capability to implement artificial intelligence techniques to sentiment analysis of tourism reviews.
  • Ability to analyse AI-based models and reconfigure the settings within defined evaluation benchmarks.
  • Skilled in the application of AI in the creation of initiatives for tourism marketing.
  • Ability to manage and analyse volumetric datasets and generate actionable insights.
  • Ability to translate relevant insights derived from data to inform decisions and communicate to the actors in tourism industry.

Core Topics and Subtopics :

Introduction to AI in Tourism

  • Concept of AI within the sphere of tourism
  • Concept of AI within the sphere of AI applications related to consumer travel
  • AI technologies, impact, applications, and ethics

Data Collection in Tourism

  • Categories of tourism data (ordered / unordered)
  • Information gathering: reviews, social networks, surveys, social media, and instruments
  • Privacy and data consent

Foundational Concepts in Data Analytics

  • Descriptive, diagnostic, predictive, and prescriptive analytics
  • Data cleaning and preprocessing
  • Introductory workshop on Excel, Python

AI Applications in Tourism

  • Conversational agents and automated personal assistants
  • Systems for making recommendations
  • Recognition of images such as tagging of landmarks
  • Predictive analytics applied to bookings

Big Data and Tourism Trends

  • Utilising Big Data for Predictive Analytics in the Tourism Industry
  • Analytics in the Information Age
  • Practical Applications from Airlines, Hospitality Services, and Destination Marketing Organisations

Machine Learning in Tourism

  • Supervised learning contrasted with unsupervised learning
  • The segmentation of customers
  • Review-based sentiment analysis

Visualisation and Reporting

  • Development of dashboards utilising Power BI and Tableau
  • Data-driven narrative construction for tourism datasets
  • Monitoring of Key Performance Indicators

Future of AI in Tourism

  • Ecosystems of intelligent tourism
  • The role of AI in sustainability
  • Emerging trends: augmented and virtual reality, digital twins, self-operating services

Weekly Breakdown / Session Plan

Week 1: Introduction to AI and Data Analytics in Tourism

  • Learning Goals: Understand the role of AI and data analytics in the tourism industry.
  • Learning Activities: View introductory video and join a discussion forum on “The Impacts of AI on the Tourism Sector Today.”
  • Key Readings: AI in Tourism: The Future is Now (Article)

Week 2: Data Collection and Management for Tourism

  • Learning Goals: Learn about types of data used in tourism and how to collect and manage them.
  • Learning Activities: Study the case on data collection techniques and complete assessment on data types.
  • Key Readings: Data Collection Methods in Tourism (Chapter)

Week 3: Big Data Analytics for Tourism

  • Learning Goals: Explore how big data analytics is applied to tourism.
  • Learning Activities: View tutorial on big data analysis and perform a team project analyzing a tourism dataset.
  • Key Readings: Big Data in Tourism (Research Paper)

Week 4: Machine Learning for Tourism Predictions

  • Learning Goals: Understand machine learning algorithms used in tourism prediction models.
  • Learning Activities: Attend recorded lectures and complete a hands-on task constructing a basic predictive model.
  • Key Readings: Machine Learning for Tourism Analytics (Book)

Week 5: AI in Personalisation and Customer Experience

  • Learning Goals: Examine how AI enhances personalisation and customer experiences in tourism.
  • Learning Activities: Discussion on AI-enhanced customer interfaces and analysis of a specific scenario.
  • Key Readings: Personalization in Tourism (Research Article)

Week 6: Tourism Forecasting with Data Analytics

  • Learning Goals: Study how data analytics is used for tourism forecasting and demand prediction.
  • Learning Activities: Attend forecasting tutorial and participate in seminar on tourism trends.
  • Key Readings: Tourism Demand Forecasting (Journal Article)

Week 7: AI in Smart Tourism Destinations

  • Learning Goals: Investigate AI and data-driven smart tourism destinations.
  • Learning Activities: Analyze smart destination case studies and view AI smart tourism video.
  • Key Readings: Smart Destinations: The Future of Tourism (Book Chapter)

Week 8: Ethical Issues and Challenges Related to AI and Data Analytics in Tourism

  • Learning Goals: Identify ethical issues and challenges of AI and data analytics in tourism.
  • Learning Activities: Examine case studies centered around privacy and ethics in data metrics, followed by discussion.
  • Key Readings: Ethics in Tourism Analytics (Article)

Week 9: AI and Data Analytics in Tourism Marketing

  • Learning Goals: Comprehend the role of AI in transforming marketing strategies within the tourism sector.
  • Learning Activities: Develop a marketing campaign using AI tools and participate in forum discussion “Marketing with AI.”
  • Key Readings: AI in Tourism Marketing (Case Study)

Week 10: Final Project and Course Review

  • Learning Goals: Apply AI and data analytics concepts in a real-world tourism project.
  • Learning Activities: Work on the final project and submit for evaluation.
  • Key Readings: Applied Data Analytics in Tourism (Textbook)

Reading Materials

Week 1: Introduction to AI in Tourism

  • Topic: Artificial Intelligence in Tourism; A Review
  • Reading Material Type: Article, Journal
  • Author / Source: Gretzel, U., et al. (2020)

Week 2: Fundamentals of Data Analytics

  • Topic: Data Analytics Made Accessible (Ch. 1–3)
  • Reading Material Type: eBook
  • Author / Source: A. Maheshwari

Week 3: AI Technologies in Travel

  • Topic: Application of AI in the Travel Industry
  • Reading Material Type: Report
  • Author / Source: World Travel & Tourism Council (WTTC)

Week 4: Big Data & Customer Experience

  • Topic: Big Data and Analytics in Tourism and Hospitality (Chapters 4 and 5)
  • Reading Material Type: Textbook
  • Author / Source: Zeng, B., et al.

Week 5: Analytics Predictivos En El Turismo

  • Topic: Forecasting and Analytics in Tourism (Reserved Sections)
  • Reading Material Type: Academic Publication
  • Author / Source: Song, H. and Li, G.

Week 6: Case Studies – The Smart Tourism Destinations

  • Topic: Smart Tourism: The Foundations and Progressions
  • Reading Material Type: Book Chapter
  • Author / Source: Gretzel, U., Sigala, M.

Week 7: Ethics & Responsible AI in Tourism

  • Topic: Ethical Implications of AI in Tourism
  • Reading Material Type: White Paper
  • Author / Source: UNWTO, OECD

Week 8: Platforms and Tools for Data Analysis

  • Topic: A Hands-on Guide to Data Analytics Tools – Power BI, Tableau, and Python
  • Reading Material Type: Self-Paced Course / Lesson Guide
  • Author / Source: Your Medium, edX and Coursera articles