Module Description:

This module explores the enabling and disruptive effects of Artificial Intelligence (AI) and Deep Learning in the context of tourism and hospitality. Students will learn the underlying concepts and acquire firsthand knowledge of intelligent systems interfaced with guests, operational functions, and novelties aimed at serving the guests. Teaching focuses on working systems such as intelligent virtual assistants, tailored recommendation systems, predictive demand forecasting, and smart tourism systems. Through the detailed case studies and group projects coupled with long-term practicum placements, students will learn to create and apply AI-based systems with a competitive advantage in a fast-changing and automating world. The course also covers the ethical and legal aspects of the technology, responsible stewardship of the information systems, and innovation that respects social and environmental boundaries (150 Hours).

Learning Outcomes:

Competences:

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

  • Employ AI and deep learning methodologies to address practical problems in tourism and hospitality industries.
  • Apply AI technology to the domains of tourism and hospitality and assess pertinent social and ecological challenges for their impact on humanity.
  • Implement ethics of AI applications in the tourism and hospitality fields and analyse its implications for social responsibility and sustainability.
  • Engage in research related to innovational AI developments and technologies.

Knowledge:

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

  • Use artificial intelligence and deep learning techniques to solve real world issues within the tourism and hospitality industries.
  • Use AI in the field of tourism and hospitality and evaluate the relevant sociological and ecological issues for their bearing on human life.
  • Apply sociological and ecological social responsibility and sustainability to AI applications in tourism and hospitality and evaluate its social impact.
  • Partake in studies associated with innovational AI breakthroughs and technologies.

Skills:

At the end of the module/unit the learner will have acquired the following skills: 

  • Utilise AI technologies to analyse and interpret data for deriving actionable insights in operational tourism and hospitality management.
  • Design and create prototypes of AI-based systems including, but not limited to, chatbots, recommendation systems, and models predicting demand to service several needs.
  • Evaluate how AI technologies are enhancing customer centricity and the overall effectiveness of the business.
  • Articulate the rationale and findings of the inquiry done in the relevant domain of the inquiry to both the experts and the general public.

Core Topics and Subtopics :

1. The Introduction to AI and Deep Learning

  • The differences between AI, ML, and Deep Learning
  • Neural networks and their types, including CNNs and RNNs
  • AI’s role in tourism innovation

2. Data Related to Tourism and Hospitality

  • Data types: customer, behavioural, and operational data
  • Gathering and preprocessing data
  • GDPR, data privacy, and moral implications of data use

3. AI in Tourism and Hospitality - Opportunities and Challenges

  • Recommender systems
  • Online customer service (Chatbots)
  • Predictive analytics powered by AI
  • Smart tourism systems

4. Deep Learning Use Cases

  • Image recognition in hospitality and transport
  • Sentiment analysis
  • Natural language processing for customer feedback and automated translation

5. Strategic Innovation

  • Developed AI frameworks
  • Business model innovation
  • Hilton and Expedia industry analysis

6. Social and Ethical Dimensions

  • Social algorithms: biases and fairness
  • Trust frameworks for algorithms and social AI
  • The socio-cultural and environmental impact

Weekly Breakdown / Session Plan

Week 1: Introduction to AI and Deep Learning

  • Objectives: Familiarise with the core concepts of Artificial Intelligence (AI) and Deep Learning (DL), and explore their applications in the tourism and hospitality industry.
  • Activities: Lecture, case study discussion, and group activity focusing on real-world examples.
  • Key Readings: Edgell et al. (2022), Chapter 1; Russell & Norvig (2020), Introduction to AI.

Week 2: Machine Learning Foundations

  • Objectives: Analyse tourism data applications and understand the principles of supervised and unsupervised learning.
  • Activities: Interactive demonstrations using machine learning models and dataset investigations.
  • Key Readings: Goodfellow et al. (2016), Chapter 5; Buhalis & Leung (2018).

Week 3: Deep Learning and Neural Networks

  • Objectives: Explore the architecture of neural networks and understand how they enhance decision-making processes.
  • Activities: TensorFlow/PyTorch coding lab and neural network simulation exercises.
  • Key Readings: Chollet (2021), Deep Learning with Python, Chapters 4–5.

Week 4: AI Applications in Tourism & Hospitality

  • Objectives: Review key AI applications such as recommender systems, chatbots, and dynamic pricing in the tourism and hospitality sector.
  • Activities: Evaluation of a case study and guest lecture by a representative from a tech startup.
  • Key Readings: Gretzel et al. (2015); Zeng et al. (2020).

Week 5: Ethics, Privacy and Responsible AI

  • Objectives: Recognise different perspectives and challenges related to AI ethics, privacy, and responsible innovation.
  • Activities: Interactive discussion and workshop on ethical dilemmas in AI applications.
  • Key Readings: Mittelstadt et al. (2016); European Commission AI Ethics Guidelines.

Week 6: Innovation Project Development & Presentations

  • Objectives: Apply acquired knowledge to propose AI-based solutions for business challenges in tourism and hospitality.
  • Activities: Peer review, group project development, and final presentations.
  • Key Readings: No new readings – focus on project research and presentation preparation.

Reading Materials

Type: Core Text Book

  • Title: Artificial Intelligence; A Modern Approach (4th edition)
  • Authors: Russell, S. & Norvig, P.
  • Publisher / Source: Pearson, 2020

  • Title: Deep Learning
  • Authors: Goodfellow, I., Bengio, Y. & Courville, A.
  • Publisher / Source: MIT Press, 2016

  • Title: Smart Tourism; Foundations and Developments
  • Authors: Gretzel, U., Sigala, M., Xiang, Z. & Koo, C.
  • Publisher / Source: Springer, 2015

Type: Supplementary Text

  • Title: Artificial Intelligence and Machine Learning in Hospitality Industry
  • Authors: Kaushal, V. & Srivastava, S.
  • Publisher / Source: CRC Press, 2021

  • Title: Robotics and AI in the Tourism and Hospitality Industry; Challenges and Opportunities
  • Authors: Ivanov, S.
  • Publisher / Source: Emerald Publishing, 2020

Type: Academic Article

  • Title: “A Review of Research into Automation in Tourism”
  • Authors: Tussyadiah, I.
  • Publisher / Source: Annals of Tourism Research, 2020

Type: Industry Report

  • Title: Travel & Tourism Development Index Report
  • Authors: World Economic Forum
  • Publisher / Source: WEF, 2022

Type: Policy Report

  • Title: Artificial Intelligence in Tourism – Current Applications and Future Trends
  • Authors: UNWTO
  • Publisher / Source: UNWTO, 2023