=Paper=
{{Paper
|id=Vol-3762/564
|storemode=property
|title=Intelligent Smart Tourism Education: AI-Based Learning for Cultural Tourism Experiments
|pdfUrl=https://ceur-ws.org/Vol-3762/564.pdf
|volume=Vol-3762
|authors=Michele Angelaccio,Michele Fasolo,Lucia Zappitell
|dblpUrl=https://dblp.org/rec/conf/ital-ia/AngelaccioFZ24
}}
==Intelligent Smart Tourism Education: AI-Based Learning for Cultural Tourism Experiments==
https://ceur-ws.org/Vol-3762/564.pdf
Intelligent Smart Tourism Education: AI-based Learning
for Cultural Tourism Experiments
Michele Angelaccio1,*,†, Michele Fasolo,1,† and Lucia Zappitelli1,†
1Dipartimento Ingegneria dell’Impresa (DII), University of Rome “Tor Vergata”, via del Politecnico 1, Rome,
00100, Italy
Abstract
Self-learning and active learning are becoming key focal points in the digital era, where the
demand for online learning is growing. This is particularly important for the education of smart
tourism guides within the context of cultural tourism. In this paper, we present some experiences
obtained within the context of the Digital Tourism Course at the University of Rome Tor Vergata
over the past two years. The focus is on enhancing self-learning skills through the use of AI
generative techniques and its role in digitalizing cultural tourism. We also provide comparisons
and discussions on the reported advantages.
Keywords
Smart cultural tourism, inclusive learning, self-learning, AI, generative models1
1. Introduction enhancing learning experiences. One of the notable
applications of AI in education is AI-assisted learning,
The integration of artificial intelligence (AI) in which leverages machine learning algorithms and
education, particularly in the field of digital tourism, is natural language processing techniques to
a growing area of interest. Xing (2022) and Cesta personalize and optimize the learning process for
(2020) both emphasize the potential of AI in individual students. This introduction aims to provide
personalizing learning experiences, with Xing an overview of AI-assisted learning, drawing insights
focusing on the design of a tourism teaching system from a survey of relevant literature.
and Cesta on the use of intelligent tools for cultural AI-assisted learning encompasses a variety of
heritage visits. Ferràs (2020) further explores the techniques and technologies aimed at tailoring
application of AI in tourism, highlighting its role in educational content, delivery, and assessment to meet
creating customized experiences. Morellato (2014) the diverse needs and preferences of learners. As
adds a new perspective, proposing an experiential highlighted by [4], AI systems can analyze vast
approach to developing digital competence in tourism amounts of educational data, including student
education, which could be enhanced by AI-driven performance, learning styles, and knowledge gaps, to
personalization and active learning. These studies generate personalized learning pathways and
collectively underscore the potential of AI in recommendations. These systems can adaptively
transforming the learning experience in digital adjust the difficulty level of learning materials,
tourism, from personalized teaching systems to the provide real-time feedback, and offer additional
creation of tailored tourist experiences. resources or exercises based on individual learning
In recent years, the integration of artificial progress and proficiency.
intelligence (AI) into education has been rapidly Moreover, AI-powered tutoring systems have
advancing, offering promising opportunities for shown great potential in providing personalized
Ital-IA 2024: 4th National Conference on Artificial Intelligence, fasolo@ uniroma2.it (M. Fasolo); lucia.zappitelli@gmail.it (L.
organized by CINI, May 29-30, 2024, Naples, Italy Zappitelli)
∗ Corresponding author.
† These authors contributed equally. © 2024 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
michele.angelaccio@uniroma2.it (M. Angelaccio);
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
support and guidance to students. For instance, provide personalized recommendations for
intelligent tutoring systems (ITS) can simulate one- activities that match the learner's current level of
on-one interactions with students, offering immediate knowledge, ensuring that they are appropriately
feedback, explanations, and hints tailored to their challenged without feeling overwhelmed or
specific learning needs [2]. Through the use of natural bored. By adapting to the learner's evolving
language understanding and dialogue generation abilities, adaptive learning systems promote
techniques, chatbot-based tutoring systems can continuous improvement and engagement in self-
engage in conversational interactions with learners, learning activities.
answering questions, clarifying concepts, and
fostering a supportive learning environment [5]. • Intelligent Tutoring Systems (ITS)
ITS leverage AI techniques to simulate one-
Furthermore, AI-assisted learning extends beyond on-one tutoring interactions, providing
individualized tutoring to collaborative and social personalized guidance, feedback, and
learning experiences. Social recommender systems support to learners. Through natural
can leverage AI algorithms to analyze learners' social language processing and machine learning
interactions, preferences, and interests, facilitating algorithms, ITS can understand the learner's
the discovery of relevant learning resources and questions, misconceptions, and learning
fostering peer-to-peer knowledge sharing [6]. Virtual goals, offering targeted explanations, hints,
collaborative environments powered by AI can enable and examples to facilitate self-directed
students to engage in collaborative problem-solving, learning. By providing immediate and
project-based learning, and group discussions, with tailored assistance, ITS empower learners to
intelligent agents providing guidance, coordination, navigate complex concepts and topics
and feedback throughout the collaborative process independently.
[3]. In this paper we outline some experiences • Recommendation Systems
obtained in the context of Digital Tourism Course at AI-driven recommendation systems analyze
the University of Rome Tor Vergata in the last 2 years. learners' preferences, past interactions, and
The focus is on improving self-learning skills with the learning objectives to suggest relevant
use of AI generative and its role in the context of resources, courses, and activities for self-
cultural tourism digitalization motivated by several directed learning. These systems can
reasons as described in [1] in which the importance of recommend educational materials, such as
academic role has been outlined. articles, videos, tutorials, and online courses,
that align with the learner's interests and
2. Impact of AI in Providing Self- goals, thereby facilitating serendipitous
discovery and exploration of new topics. By
Learning Activities curating personalized learning pathways,
The impact of AI in providing self-learning activities is recommendation systems empower
profound and multifaceted. Here are several key ways learners to take ownership of their learning
in which AI influences self-learning: journey and pursue areas of interest
autonomously.
• Personalized Learning Paths • Natural Language Processing (NLP)
AI can analyze vast amounts of data about a NLP technologies enable interactive and
learner's preferences, strengths, weaknesses, and conversational interfaces for self-learning
learning styles to tailor educational content and activities, such as chatbots and virtual
activities to their individual needs. By assistants. Learners can engage in dialogue
understanding the learner's pace and level of with AI-powered agents to ask questions,
understanding, AI algorithms can suggest seek explanations, and receive feedback in
appropriate resources, exercises, and challenges, natural language. By fostering interactive
thereby enabling self-directed learning that is and responsive learning experiences, NLP-
customized to each learner. driven systems support self-directed
• Adaptive Learning Systems inquiry, reflection, and problem-solving,
AI-powered adaptive learning systems enhancing learners' autonomy and
dynamically adjust the difficulty and pace of confidence in their ability to learn
learning activities based on the learner's independently.
performance and progress. These systems can
Overall, AI plays a transformative role in enabling and destinations, while those interested in immersive
enhancing self-learning activities by providing experiences may be directed to resources on virtual
personalized guidance, adaptive support, curated reality technologies.
resources, interactive interfaces, and actionable
feedback. By leveraging the capabilities of AI 3.1.2. Chatbot Assistance
technologies, learners can engage in self-directed To facilitate seamless navigation and access to
learning that is tailored to their individual needs, resources, the platform features a chatbot interface
preferences, and aspirations, thereby empowering that serves as a virtual assistant. Learners can interact
them to become lifelong learners mastering new with the chatbot using natural language queries to
skills, acquiring knowledge, and achieving their request specific information or resources. For
educational goals autonomously. instance, a learner interested in learning about the use
of augmented reality in cultural tourism may type "AR
3. Case Description: Self-Learning in applications" into the chatbot interface. The chatbot
Digitalization of Cultural Tourism then retrieves relevant PDF documents, articles, and
videos from the platform's database and presents
Digitalization of cultural routes is a key point used in them to the learner for exploration.
the education of new generation of tour operators and
smart guides. Self-Learning AI powered activities 3.1.3. Accessing PDF Resources
provide in this case a promising means to guarantee a
flexible and dynamic learning process suitable in the One of the key functionalities of the chatbot is its
new tourism era and in particular cultural tourism ability to provide access to PDF resources on demand.
case. To demonstrate the applicability of AI based Learners can request PDF documents on specific
education in cultural tourism, we consider three topics by simply typing keywords or phrases related
examples used in the Rome Tor Vergata University. to their interests. For example, a learner curious about
the impact of digitalization on cultural heritage
3.1. Self-learning Platform for Cultural preservation may type "heritage preservation" into
the chatbot interface. The chatbot then searches the
Travel Blog Design
platform's repository for PDF documents, reports, or
A first Example of Digital Tourism learning activity research papers related to the topic and presents
is the digitalization of cultural routes emerging from them as downloadable resources.
the need to explore archeo sites and cultural heritage
assets in a huge number of ways. As example of self- 3.1.4. Interactive Learning Activities
learning platform, we discuss the case of cultural
In addition to accessing static resources, learners
travel blog designed for walking routes and denoted
can engage in interactive learning activities to deepen
as cultural travel blog in which information is
their understanding of digitalization in cultural
designed to offer comprehensive resources and
tourism. These activities may include quizzes,
interactive activities focused on the digitalization of
simulations, virtual tours, and collaborative projects,
cultural tourism. Learners have access to a variety of
allowing learners to apply theoretical concepts in
multimedia content, including articles, videos, case
real-world scenarios and gain hands-on experience in
studies, and tutorials, covering topics such as digital
digital cultural heritage management.
marketing strategies for cultural attractions, virtual
reality experiences in heritage sites, and augmented 3.1.5. Progress Tracking and Feedback
reality applications for guided tours.
Throughout their learning journey, learners can
3.1.1. Personalized Learning Paths track their progress and performance using built-in
analytics and assessment tools. AI algorithms analyze
Upon accessing the platform, learners are
learner interactions, quiz scores, and completion rates
prompted to create a profile where they can specify
to provide personalized feedback and
their interests, goals, and prior knowledge in the field
recommendations for further learning. Learners
of cultural tourism. AI algorithms analyze this
receive insights into their strengths and areas for
information to generate personalized learning paths
improvement, empowering them to adapt their
tailored to each learner's preferences and learning
learning strategies and goals accordingly.
objectives. For example, learners interested in digital
marketing may receive recommendations for articles
and tutorials on social media advertising for cultural
The self-learning platform for digitalization of
BEFORE Master
cultural tourism offers a flexible and engaging Marketing, Communication
educational experience, enabling individuals to
TRAVEL Skills
Destination
Designer
acquire knowledge and skills at their own pace and
Planning
Narrative
convenience. By harnessing the capabilities of AI CULTURAL
Destination LOCAL knowledge and Technical,
Practical Skills Experience Skills
Tour TRAVEL Skills
technologies and interactive resources, the platform
Local Operator
People
Cultural
Focus
Expert
empowers learners to explore the intersection of Collaborate
culture, technology, and tourism and become
Travel
informed advocates for sustainable and inclusive Guide
digital practices in cultural heritage preservation and
promotion.
Destination
AFTER Master
Designer
LESS Planning
Destination AI Travel
3.2. Self-learning activities for local Smart
Online learning
chatBot
AI powered skills Self learning
+ engangement Trained
+ economic benefits Local
Cultural Operators
People Cultural
Expert
AI Travel Guide
As another example of the application of a self- Smart Tour Operator
Skills
Master
Organization
AI
Expert
learning platform, we highlight the case of Archeo AI
Models
Tutorial, implemented within the context of the AI generated
Packet
ERASMUS+ project ADHOC. The platform's role is to AI Builder
enhance accessibility for users with disabilities. In this
context, the platform has been equipped with a Figure 1: AI-Power Master of Local Cultural Guides
dynamic generative script for the text-to-speech Overview
function, allowing it to be seamlessly integrated into
the original archaeological guide. Obviously, this 3.3. Accessing a PDF Book on Cultural Routes
generation can be put in the form of NLP by taking
in Rome
advantage from a chatbot interacting with PDF
resources. This approach showcases how AI In this latter example, we aim to highlight an
generative interaction with PDF resources can serve instance of an AI-powered application utilizing
as a viable solution for self-learning activities aimed at advanced AI-assisted self-cultural guidance. The
educating cultural guides. This is particularly valuable learner communicates their interest in cultural routes
in contexts where there is a need to extend cultural in a densely populated cultural destination such as
tourism learning to students with disabilities, as part Rome through a natural language query to the
of an inclusive learning program. For instance, in the chatbot. The chatbot recognizes the learner's request
case of translating cultural content into LIS (Italian and responds by asking a PDF book.
Sign Language), the learning program is tailored for Exploring historical cities like Rome can now
students who are already proficient in using LIS. benefit from a variety of digital tools (applications,
Figure 1 illustrates the impact of AI-powered augmented reality, chatbots, and interactive maps)
learning methodologies on the education of local that facilitate immediate access to the dense and
cultural guides. The diagram depicts the gains and hyper-dimensional fabric of data, stories, and paths
benefits that could be achieved through the Master's related to the cultural components of the urban
program (see also 8]). landscape. Particularly, the integration of intelligent
chatbots and interactive maps, programmed with
extensive databases of information, represents an
innovative breakthrough in the cultural tourism
sector capable of offering users immediate answers to
specific questions, recommendations tailored to their
interests and needs, as well as real-time updates on
events in the explored context, significantly enriching
the personal experience and making it unique and
rich. In this regard, a case study aimed at optimizing
the touristic exploration of Rome is proposed, based
on the innovative integration of an advanced chatbot,
built on generative artificial intelligence technologies,
and Leaflet, an open-source platform for creating
interactive maps. This synergy specifically aims to learning experiences through its technological
offer a highly personalized tourist experience, within features (intelligent tutoring systems and chatbots)
the scope of themes chosen by the user, encompassing that cater to the needs for personalization and
a wide spectrum of cultural components of the urban adaptability. This work particularly investigates the
landscape, from artistic and historical to importance and impact of AI, with a special reference
enogastronomic elements. This system, named to AI-assisted learning in the field of cultural tourism.
GuidaTuristicaAI (or AI-CH-Tour-Map), includes in its There are several ways in which AI can be enabled for
descriptions information drawn from a vast corpus of better personalization and improvement of the
documents, ranging from books by illustrious visitors, educational experience, namely customizing learning
literary works, and musical compositions, to paths, advanced tutoring systems, recommendation
enogastronomic guides about Rome over the mechanisms, and the use of natural language
centuries. Upon interacting with GuidaTuristicaAI, the processing to enhance interaction and support for
user is invited to select one or more themes of interest students. The research has specifically focused on the
to further personalize their exploration of the city. If use of self-learning platforms in the production of
the choice falls, for example, on literary works about blogs dedicated to cultural travel, emphasizing the
Rome as a thematic filter, the chatbot searches its PDF importance of digitalizing cultural routes. Particular
document database to select those including literary attention was given to specific use cases implemented
references to Rome, extracted from works by famous at the University of Rome "Tor Vergata", highlighting
authors who have described the city in their visits or how AI solutions can enable self-learning and
narrative and poetic works. In the case study, it is accessibility for users with disabilities, aiming to
imagined that an art history student plans a visit to ensure a better experience in their training for future
Rome following the footsteps of Giuseppe Vasi. The professionals. AI can help and enhance a series of
chatbot suggests a series of publications. For example, activities conducted in self-learning through personal
they can download the PDF of one of the volumes from guidance, adaptive support, and interactive interfaces
the open archive.org site or another to resources. It allows students to follow a self-
source:( https://ia801006.us.archive.org/7/items/te regulated learning path that, according to their needs,
sorosacroevene02vasi/tesorosacroevene02vasi.pdf ) preferences, and personal goals, enables approaches
. Using the web application, the user receives a to lifelong learning, which can empower them to
personalized itinerary with the sites described by acquire new skills and knowledge entirely
Vasi. The generated interactive map allows him to autonomously. Such flexible and interactive self-
easily navigate from one place to another, while the learning modalities can be effectively extended into
chatbot provides cultural and historical insights. The digital tourism, facilitating experiences by any user in
Leaflet mapping platform plays a crucial role in the consuming cultural landscapes in a more accessible
implementation of this thematic customization. The and engaging way.
dynamism of the real-time interaction between the
user and GuidaTuristicaAI can indeed be enriched
with specific points of interest identified on the map
related to other themes selected by the user, allowing References
them to discover the city through different
[1] Amato, Flora, et al. "Ai-powered learning:
interpretative and perceptual lenses. For example, in
Personalizing education for each student." 3rd
accord to the literary theme, markers can be added to
CINI National Conference on Artificial
places mentioned in novels or poems, with short
Intelligence (ITAL-IA 2023), in Pisa, Italy. 2023.
excerpts or quotes visible by clicking on the respective
[2] J. R. Anderson et al. “Cognitive tutors: Lessons
markers, emotionally connecting them to the places
learned”, Journal of the
through the words of authors who have immortalized
Learning Sciences (1985), pp. 167–207.
them in their works. Moreover, information can be
doi:10.1207/s15327809jls0402_2.
provided on dining venues and typical Roman dishes
[3] T. De Jong and A. W. Lazonder. “Designing
and wines that can be paired with them.
Virtual Collaborative Learning Environments”.
In: Learning, Design, and Technology: An
4. Conclusions International Compendium of Theory, Research,
From the outset and in the coming years, AI Practice, and Policy. Ed. by M. Spector, B. B.
promises to play a decisive innovative role in the field Lockee, and M. D. Childress. Springer, 2016, pp.
of educational pathways, transforming and enhancing 1–23. doi: 10.1007/978-3-319-17727-4_179-1
[4] A. Smith. “AI in Education: Promises and Transactions on Emerging Topics in Computing
Pitfalls”. In: Journal of Edu- 5.2(2017), pp.208–219.
cational Technology 45.3 (2020), pp. 321–335. [7] https://projectadhoc.files.wordpress.com/202
doi:10.1080/0000000X.2020.1234567. 2/12/ad-hoc-italian.pdf
[5] X. Wang et al. “Designing chatbots for tutoring: A [8] Angelaccio Michele, Zappitelli Lucia, “Active
collaborative approach”. In: International Learning in Teaching Digital Tourism:
Journal of Artificial Intelligence in Education preliminary results through Online Travel
29.4 (2019), pp. 533–574. doi: 10.1007/s40593- Business Simulation.” In: eLmL 2023, The
019-00192-y. Fifteenth International Conference on Mobile,
[6] J. Yu et al. “Social recommendation for e-learning Hybrid, and On-line Learning. IARIA, 2023. p. 1-
systems: A survey of the state-of-the-art and 4.
future research directions”. In: IEEE