=Paper=
{{Paper
|id=Vol-3879/AIxEDU2024_paper_37
|storemode=property
|title=Redefining Education: A Personalized AI Platform for Enhanced Learning Experiences
|pdfUrl=https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_37.pdf
|volume=Vol-3879
|authors=Daniele Schicchi,Davide Taibi
|dblpUrl=https://dblp.org/rec/conf/aixedu/Schicchi024
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==Redefining Education: A Personalized AI Platform for Enhanced Learning Experiences==
https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_37.pdf
Redefining Education: A Personalized AI Platform for
Enhanced Learning Experiences
Daniele Schicchi1,∗,† , Davide Taibi1
1
Institute for Education Technology, National Research Council of Italy, Palermo, Italy
Abstract
The PROSPETTIVA project aims to improve secondary education in Sicily by integrating AI technologies to
promote active learning and AI literacy among students and teachers. This paper provides an overview of the
PROSPETTIVA platform, a web-based educational tool designed to offer personalized learning experiences using
advanced Large Language Models (LLMs). The platform encourages controlled interactions with AI to prevent
surface-level learning and promotes critical thinking by enabling students to engage with AI in a structured
manner. By aligning with pedagogical objectives and incorporating teacher feedback, the project aims to establish
a meaningful use of AI in education, supporting a deeper understanding of concepts and encouraging reflective
engagement. The platform’s features, such as summarization and simplification, have been carefully selected
based on performance metrics to ensure a high-quality educational experience. Initial results suggest the potential
of this approach in improving learning outcomes and reducing educational inequalities in the region. Future
research will focus on refining the platform and expanding its functionalities based on user feedback.
Keywords
Human-AI collaboration, AI in Education, Large Language Models, Personalized Learning
1. Introduction
The use of Artificial Intelligence (AI) has permeated the educational sector, transforming traditional
teaching and learning methods into dynamic and interactive experiences [1, 2, 3, 4]. In recent years,
the development and integration of Large Language Models (LLMs) like OpenAI’s ChatGPT have
further accelerated this trend. These advanced AI tools understand, generate, and contextualize natural
language, and are being used in various educational contexts to reshape the way students and educators
interact with content.
LLMs are transforming education in three main areas: learning, teaching, and administration. Regard-
ing learning, AI allows for personalized learning paths that adjust to each student’s unique needs,
creating an environment that caters to individual learning styles and paces. Adaptive assessments and
interactive tools also enhance the learning experience. In terms of teaching, AI-powered tutors offer
immediate assistance and guidance, providing real-time feedback to help students comprehend complex
concepts without constant teacher intervention. AI also enhances teaching resources by generating
supplementary material, suggesting different instruction strategies, and streamlining the teaching
process. In administration, AI automates routine tasks like grading, scheduling, and report generation,
enabling educators to focus more on interactive and value-adding activities with their students.
The use of AI in education is an ongoing area of exploration and innovation. One of the main
challenges is ensuring that these technologies are used constructively to support deeper learning rather
than being used for superficial problem-solving. For example, students often use language models like
ChatGPT as a quick way to get answers, but this approach does not encourage a thorough understanding
or critical thinking. As a result, students may end up with shallow learning experiences, where they
only have a superficial grasp of concepts without meaningful engagement or retention.
AIxEDU: 2nd International Workshop on Artificial Intelligence Systems in Education, November 25-28, 2024, Bolzano, Italy
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open daniele.schicchi@itd.cnr.it (D. Schicchi); davide.taibi@itd.cnr.it (D. Taibi)
Orcid https://orcid.org/0000-0003-0154-2736 (D. Schicchi); https://orcid.org/0000-0003-0154-2736 (D. Taibi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
The PROSPETTIVA Project, supported by the Sicilian Regional Government, aims to address this
issue by shifting the focus from using LLMs merely as question-answering systems to employing them
as intelligent tutors capable of fostering active learning. By promoting AI literacy among both teachers
and students, the project seeks to equip learners with the skills necessary to engage in a constructive
dialogue with AI tools. This shift can stimulate engagement, enhance critical thinking, and promote
deeper retention of knowledge, ultimately enriching the educational experience. Moreover, the project
will provide a web platform as a final outcome, which students can use to enhance their learning path,
using artificial intelligence as a support for active learning rather than just a problem solver.
The objective of this paper is to describe the PROSPETTIVA platform in detail, highlighting its
core aspects and functionalities. The platform is designed not only to enrich students’ educational
experiences but also to instill a conscious and reflective use of AI in learning contexts. Through this
initiative, we aim to demonstrate how the strategic use of LLMs can support a profound and meaningful
learning journey rather than being limited to a quick problem-solving approach. By enhancing students’
competencies across various dimensions, the platform helps move beyond the superficial use of AI and
lays the groundwork for a more sophisticated and aware engagement with these technologies.
The structure of this paper is as follows: The Introduction provides an overview of AI in education
and sets the context for the PROSPETTIVA project. The Literature Review covers previous research
on AI in education, including existing AI literacy initiatives and platforms targeting secondary school
students. The Project section outlines the PROSPETTIVA project’s background, objectives, and expected
outcomes. The Methodology describes the platform’s design, participant selection, and the educational
strategies employed. The Results focus on initial findings and platform usage. The Discussion interprets
the results in light of project objectives and theoretical frameworks. Finally, the Conclusion summarizes
key insights and suggests directions for future research.
2. Literature Review
The use of Artificial Intelligence (AI) in education has received significant attention for its potential to
transform traditional teaching and learning processes. AI tools offer personalized learning experiences,
support for teachers, and enhanced classroom management. Alkan [5] highlights that adaptive learning
systems personalize educational content by assessing students’ needs and delivering tailored resources.
This approach enables a more effective learning experience by addressing individual strengths and
weaknesses. Similarly, AI aids in automating tasks such as grading and feedback generation, allowing
educators to focus on instructional strategies and student engagement [6]. An emerging trend is the
use of intelligent tutoring systems (ITS). These tools enhance cognitive skills and logical reasoning
by providing interactive learning experiences [7]. AI has been applied in education in various ways,
ranging from tracking students’ learning paths [8] to creating student models based on their skills, as
well as educating students on addressing the pitfalls of social media [9], and more
Recently, the emergence of conversational generative AI models (GAI) such as ChatGPT has unlocked
opportunities for various applications that were previously challenging to implement. They have been
integrated into classrooms to assist in various ways. These tools support writing instruction by helping
to generate outlines, revise drafts, and provide real-time feedback, allowing students to focus on complex
analytical tasks [10]. Institutions such as Harvard’s Division of Continuing Education utilize ChatGPT
to promote digital literacy and ethical AI use, emphasizing its role as a supplementary tool rather than
a replacement for human instruction [11]. Furthermore, OpenAI’s educational guides suggest using
these models to facilitate creative thinking, refine arguments, and maintain ethical standards [12].
In early education, GAI models assist in the development of reading and writing skills by identifying
and correcting grammatical errors, thereby enhancing language proficiency. Educators can also use
these models to develop animated educational content, making learning more interactive and accessible
[13]. In middle and high school education, GAI enriches the learning experience by creating interactive
visual content and tools that simplify complex theoretical subjects. This approach is particularly
effective when combined with metaverse technologies, providing virtual experiences in subjects such as
history and geography [14, 15, 16]. In higher education, GAI can aid in visualizing abstract and complex
topics, improving students’ understanding and engagement [17, 18]. Furthermore, GAI serves as a
valuable tool for distance learning and inclusivity, offering customized text-to-speech and speech-to-text
capabilities, thereby ensuring equal learning opportunities for individuals with disabilities [19, 20].
One important example of the success of using GAI is in medical education, where AI applications
are crucial for professional development. This technology supports medical research by analyzing large
datasets, allowing students to stay updated on new treatments and healthcare trends [21]. GAI can
also enhance the development of clinical communication skills through simulated patient interactions
[22]. Additionally, GAI provides immediate evaluation and feedback on both theoretical knowledge
and practical skills, guiding students to improve specific areas of their training [23].
Moreover, GAI offers an innovative approach to revitalizing historical knowledge by digitalizing these
materials. In this sens, GAI can transform static content into interactive learning experiences, making
them more engaging and relevant [24]. This technology allows for the integration of historical teaching
methods with modern educational paradigms, creating a comprehensive academic framework that
accommodates both traditional and contemporary learning approaches [25, 26].
Finally, General Artificial Intelligences (GAIs) are not only beneficial for young education but they
have also been proven to be valuable in transforming adult education. They can be utilized as tools for
skill development and can help address career ambiguity in youth by providing personalized career
guidance. This is achieved through analyzing job trends, preferences, and skills, and by supporting
learning with customized question selection and feedback in fields such as competitive programming
[27]. This targeted approach enhances both learning and career growth.
3. The Project
The PROSPETTIVA Project was initiated in response to a research proposal by the Sicilian Region Gov-
ernment, aiming to explore and enhance the role of Artificial Intelligence (AI) in secondary education
across Sicily. Sicily, an island in Southern Italy with a population of 4,784,852, is home to an extensive
educational network comprising 4,865 comprehensive schools, 121 main first-grade institutes, and 2,661
second-cycle institutions. The project is a collaborative endeavor between the ITCG Carlo Alberto Dalla
Chiesa of Partinico and the Institute for Educational Technology in Palermo, a division of the National
Research Council of Italy. Together, these institutions have launched a groundbreaking initiative to
integrate advanced AI technologies into the region’s educational framework.
The name PROSPETTIVA, meaning ”prospect” in Italian, encapsulates the project’s forward-thinking
mission. It stands for ”Progetto di Supporto per l’Educazione Personalizzata tramite Tecnologie In-
telligenti Avanzate” (Project for Supporting Personalized Education through Advanced Intelligent
Technologies). This initiative is rooted in a vision of transforming education to be more adaptive, inclu-
sive, and effective. Its primary objectives include reducing school dropout rates, improving academic
performance across various subjects, and enhancing the overall quality of education by leveraging
AI-based tools in the learning process.
A key focus of the project is on fostering students’ critical awareness of AI technologies and their
applications. By equipping students with a better understanding of AI’s role in society, the project
seeks to develop informed, tech-savvy individuals. Simultaneously, the project emphasizes AI literacy
training for teachers, empowering them to integrate AI tools into their teaching methods effectively
and prepare for the evolving challenges and opportunities AI brings to education.
The centerpiece of the PROSPETTIVA Project is the development of an innovative AI-powered web
platform designed to transform learning experiences. This platform will be tailored to meet the individ-
ual needs of students through continuous monitoring of their progress and performance. By employing
AI-driven features, the platform will offer personalized learning pathways, interactive tools to deepen
concept comprehension, and adaptive resources to address specific learning gaps. For educators, the
platform will serve as a powerful tool for gaining insights into student engagement and academic
development, enabling them to make data-informed decisions to enhance their teaching strategies.
Figure 1: Architecture of the PROSPETTIVA Platform. The platform allows users to interact with the AI through
a web-based interface, where they can access various learning support functionalities tailored to educational
tasks. These functionalities are implemented using prompt engineering techniques to ensure alignment with
pedagogical objectives. The storage module records all AI-student interactions, enabling the generation of
analytics to monitor and analyze student behavior, engagement, and learning progress.
Prompt Engine
Storage Analytics
Web App
Through this comprehensive initiative, the PROSPETTIVA Project aspires to create a scalable model for
integrating AI in education, paving the way for a future where technology serves as a cornerstone for
personalized, effective, and inclusive learning in Sicily and beyond.
4. Methodology
For the PROSPETTIVA project, the final outcome will be a web-based application. It will be the core
technological platform, ensuring easy access through various devices such as desktop computers,
tablets, and smartphones. The platform’s design prioritizes user accessibility and functionality, making
it suitable for diverse educational settings. The primary functions of the platform include enabling
students to interact directly with AI and providing teachers with tools to monitor students’ behavior
and progress.
The platform is organized into four five modules, whose description is illustrated in figure 1.
4.1. User-AI Interaction
The platform features a chat-like interface specifically designed to facilitate meaningful interactions
between students and Artificial Intelligence. While its layout and functionality resemble traditional
chat environments, the dialogue with the AI is intentionally restricted to ensure its alignment with
educational goals. Rather than offering open-ended conversations, the platform provides a curated
set of specialized functionalities powered by an advanced Large Language Model that can be carefully
chosen in collaboration with educators. These features are meticulously selected based on rigorous
performance evaluations that measure the effectiveness of LLMs in executing particular educational
tasks.
Currently, the platform includes functions such as Simplification and Summarization, areas where
LLMs have demonstrated exceptional proficiency and reliability [28]. These tools enable students to
break down complex ideas into more digestible forms and condense extensive information into concise
summaries, fostering deeper understanding and efficient learning. As additional use cases are identified
and validated, the feature set may expand, always adhering to the principle of enhancing educational
Figure 2: Interface of the PROSPETTIVA Platform. The interface is designed as a structured chat environment
with controlled functionalities accessible through pop-up windows. These predefined features guide students in
utilizing the platform to support their learning tasks, ensuring a focused and purposeful interaction with the AI
while maintaining the educational objectives of the platform.
outcomes.
The deliberate exclusion of free-chat functionality reflects the platform’s commitment to maintaining a
focused and purposeful learning environment. Open-ended interactions with LLMs, while potentially
engaging, often risk deviating from the educational objectives, introducing ambiguities, or inadvertently
reinforcing misconceptions. By narrowing the AI’s role to well-defined educational tools, the platform
ensures that students engage with AI in a manner that supports structured learning processes. This
approach not only prevents misuse but also underscores the platform’s dedication to constructive,
meaningful engagement with technology, ensuring it remains a catalyst for academic growth rather
than a shortcut or distraction.
A representation of the user interface is given in figure 2.
4.2. AI Engine
The AI Engine serves as the central module that orchestrates and oversees all artificial intelligence
processes within the platform. This pivotal component ensures that AI interactions are efficient, reliable,
and tailored to meet the platform’s educational objectives. Our decision to utilize open-weight LLMs,
rather than proprietary options like ChatGPT, stems from their accessibility and proven efficacy across
a wide array of tasks. By leveraging open-weight models, we maintain flexibility, transparency, and
cost-efficiency, enabling customization to suit the specific needs of this educational context.
The AI Engine is designed with adaptability in mind and is capable of integrating various Large Language
Models to ensure seamless switching between them as needed. This modular architecture not only
enhances performance but also allows for the exploration and incorporation of future advancements in
LLM technology. Serving as the computational backbone, the AI Engine generates responses, facilitates
structured dialogues with students, and ensures that the platform’s functionality remains robust and
responsive.
From a technical perspective, the AI Engine employs the Ollama framework, a platform optimized
for running natural language models locally. Ollama’s ability to execute AI models directly on users’
machines eliminates the need for external servers or cloud-based services, thereby granting greater
control over data and enhancing privacy. This local deployment significantly reduces response latency,
creating a smoother and more efficient user experience. Furthermore, Ollama’s ability to optimize
resource utilization on local machines makes it an ideal choice for organizations aiming to balance
performance with operational independence.
Currently, the AI Engine operates on a local setup equipped with two Nvidia RTX 3090 graphics cards,
boasting a combined total of 48 GB of graphic memory. This powerful configuration allows the system
to handle complex natural language processing tasks with ease, ensuring that students receive prompt,
accurate, and contextually appropriate responses. As the platform evolves, the AI Engine’s modular
design and technical infrastructure provide a solid foundation for scaling and integrating even more
sophisticated models and features.
4.3. Prompt Engine
This module uses prompt engineering to refine input-output interactions, ensuring that the model’s
answers align with the defined objectives. The prompt engineering process incorporates specific
constraints, contextual cues, and task-based guidance to guide the Large Language Model (LLM) in
generating outputs that align with educational goals. This method effectively reduces the generation of
irrelevant content, improves the relevance of responses, and maintains the expected cognitive rigor in
educational contexts. As a result, the module provides a robust framework for ensuring that the LLM’s
responses support a more focused and purposeful interaction.
The prompt engine has been developed to facilitate the introduction of new prompts that enable new
functionalities given by the LLM. Many of these functionalities are currently under development and
have been chosen according to the teacher’s suggestions.
We plan to use several prompt patterns, such as the persona pattern, the scenario pattern, and the
instruction pattern. The Persona pattern is a strategic framework aimed at enhancing interactions
with large language models through the use of role-playing. This method allows for the customization
of outputs from the language model by restricting the model to respond based on a predetermined
perspective. The Persona pattern is developed to tailor outputs according to user expectations and
requirements. The Scenario pattern, focuses on constructing detailed scenarios that simulate real-world
contexts, which helps frame the interaction in a situational context rather than a purely informational
one. By providing the LLM with a scenario-based setup, the model is encouraged to consider the
broader context of a problem, thus generating more nuanced and contextually appropriate responses.
For example, in an educational setting, a scenario might describe a classroom environment, a learning
challenge, or a particular student’s question. The Instruction pattern involves the explicit use of step-
by-step instructions and task-specific prompts to direct the model’s behavior. It is particularly effective
for tasks requiring detailed outputs, such as problem-solving, coding, or data analysis. By providing
clear and structured instructions, this pattern reduces ambiguity and ensures that the LLM follows a
systematic approach to produce results that adhere closely to the outlined steps.
The prompt engine has been developed to facilitate the introduction of new prompts that enable new
functionalities supported by the LLM. Many of these functionalities are currently under development
and have been chosen based on feedback from educators. The module can offer a more flexible and
contextually adaptive framework, ensuring that the LLM’s responses are aligned with the immediate
query and the overarching educational and developmental objectives.
5. Storage
The platform securely stores all interactions between students and AI to ensure data integrity and
enable continuous improvement of its features based on real-world usage. For the underlying data
storage, the system utilizes a MySQL database management system (DBMS). MySQL was selected for its
robust support for relational data, high scalability, and strong data integrity features, making it suitable
for handling structured educational data. The database schema includes detailed tables to capture
student profiles, learning activities, AI interactions, and feedback. Each interaction is timestamped
and linked to both the student and session ID, allowing for detailed longitudinal analysis of individual
learning paths and trends across different cohorts.
In addition to MySQL, the platform leverages Redis as an in-memory data structure store to handle
the caching of chat data during active sessions. Redis is used to ensure that the chat interactions
between students and the AI model are accessible with minimal latency, providing a seamless and
responsive user experience. By storing chat states in Redis, the platform can efficiently manage and
update ongoing conversations, reducing the load on the MySQL database for frequently accessed data.
At the end of each session, the Redis cache is flushed into the MySQL database to ensure permanent
storage of all relevant interaction data. This synchronization mechanism guarantees that even if a
session is interrupted or terminated unexpectedly, no data is lost.
By combining MySQL’s structured data storage capabilities with Redis’s efficient caching mechanisms,
the platform provides a robust and scalable storage solution that not only ensures data security but also
enhances the responsiveness and performance of AI-driven educational interactions.
5.1. Data Analysis
This module plays a crucial role in supervising student behavior and tracking their learning progress,
serving as a powerful tool for educators and researchers alike. By analyzing data generated through
interactions with the platform, the module provides a wealth of insights into how students engage
with the AI’s functionalities. Teachers can explore detailed patterns of engagement, including which
features are used most frequently and where students tend to encounter difficulties. This granular
level of understanding enables educators to identify common learning challenges and address them
proactively.
By monitoring trends and pinpointing areas of struggle, educators can offer tailored interventions, such
as personalized feedback, additional resources, or alternative instructional strategies. These targeted
measures not only address individual learning gaps but also improve overall student outcomes by
ensuring that all learners receive the support they need to succeed.
The insights generated by this module extend beyond individual student support, influencing broader
instructional strategies. Educators can leverage real-time data to adjust and refine the curriculum
dynamically, aligning it more closely with students’ needs. This turns the traditional teaching process
into a flexible, responsive system that evolves based on actual student performance and engagement.
By shifting from a fixed, one-size-fits-all approach to an adaptive model, the teaching process becomes
more effective and student-centered.
Furthermore, this feedback loop fosters a culture of continuous improvement, where educators and
researchers collaborate to optimize the learning environment. The integration of real-time analytics
ensures that instructional design remains aligned with the student’s evolving needs, empowering them
to achieve their full potential. This system not only enhances the immediate learning experience but
also contributes to the development of more effective educational methodologies over time.
6. Discussion
The initial implementation of the PROSPETTIVA platform has provided valuable insights into the
potential of AI-driven tools in enhancing educational outcomes. The platform demonstrates a significant
shift in how students engage with learning materials, emphasizing the need for critical and reflective
interaction with AI technologies rather than superficial problem-solving.
One key finding is the importance of structured AI interactions. By curating functionalities such as
summarization and simplification, the platform fosters deeper comprehension of concepts and avoids
the pitfalls associated with unrestricted AI use, such as the generation of irrelevant or misleading
content. This aligns with the pedagogical objective of promoting meaningful learning and suggests
that the intentional design of AI tools is essential in educational contexts.
The role of educators in shaping the platform has also proven critical. Their involvement in the
selection and refinement of features ensures that the technology addresses real classroom challenges
and integrates seamlessly with existing teaching methodologies. This collaboration highlights the
value of human-AI partnership in developing tools that are both effective and practical for educational
settings.
Furthermore, the platform’s data-driven approach to monitoring student interactions has provided
actionable insights into learning behaviors. By analyzing usage patterns and identifying common
challenges, educators can adopt targeted interventions to address individual and group learning needs.
This feedback loop not only enhances the immediate educational experience but also informs long-term
improvements in curriculum design and instructional strategies.
Despite these promising outcomes, several challenges remain. The reliance on specific functionalities,
such as simplification and summarization, may limit the breadth of the platform’s applications. Future
iterations should consider expanding the feature set to include tools for fostering creativity, collaboration,
and higher-order thinking skills. Additionally, ensuring equitable access to the platform, particularly in
under-resourced educational settings, will be vital for its widespread adoption and success.
Finally, the ethical considerations surrounding AI in education warrant ongoing attention. Issues such
as data privacy, algorithmic bias, and the potential for over-reliance on technology must be addressed
proactively to ensure that the platform supports inclusive and fair learning environments.
In conclusion, the PROSPETTIVA platform represents a significant advancement in the integration of
AI into education. Its focus on personalized, reflective, and critical engagement with technology sets a
strong foundation for future research and development. As the project evolves, it will be essential to
build on these findings, leveraging emerging technologies and pedagogical insights to create even more
impactful learning experiences.
7. Conclusion
This paper describes the design, development, and initial implementation of the PROSPETTIVA platform,
an AI-driven educational tool intended to enhance personalized learning experiences in secondary
education. By integrating Large Language Models (LLMs) into a purpose-driven platform, the project
aims to move beyond superficial uses of AI, encouraging deeper learning and critical engagement with
technology. The project emphasizes the importance of AI literacy for both students and educators,
equipping them to use AI tools consciously and constructively.
Initial findings from the project suggest that the platform has the potential to significantly improve
student learning outcomes by adapting to individual learning needs and providing tailored support. In
addition, involving teachers in the platform’s design process ensures that its features are aligned with
real classroom challenges, enhancing its relevance and usability in diverse educational contexts.
Future research will focus on refining the platform’s AI functionalities, exploring the long-term
impact of such technologies on student engagement and learning, and evaluating the effectiveness
of AI in fostering critical thinking skills. As the PROSPETTIVA project evolves, it aims to serve as a
model for implementing AI in education, promoting a balanced and ethical integration of advanced
technologies in learning environments.
8. Acknowledgments
Daniele Schicchi acknowledges funding from the European Union PON project Ricerca e Innovazione
2014-2020, DM 1062/2021.
References
[1] D. Schicchi, G. Pilato, Wordy: A semi-automatic methodology aimed at the creation of neologisms
based on a semantic network and blending devices, Advances in Intelligent Systems and Computing
611 (2018) 236 – 248. doi:10.1007/978- 3- 319- 61566- 0_23 .
[2] D. Schicchi, B. Marino, D. Taibi, Exploring learning analytics on youtube: a tool to support students
interactions analysis, in: ACM International Conference Proceeding Series, 2021, p. 207 – 211.
doi:10.1145/3472410.3472442 .
[3] C. Limongelli, D. Schicchi, D. Taibi, Enriching didactic similarity measures of concept maps by a
deep learning based approach, in: Proceedings of the International Conference on Information
Visualisation, volume 2021-July, 2021, p. 261 – 266. doi:10.1109/IV53921.2021.00049 .
[4] G. Casalino, G. Castellano, P. Ducange, M. Fazzolari, R. Pecori, G. Zaza, Leveraging explainable
ai methods and tools for educational data, in: International Conference on Higher Education
Learning Methodologies and Technologies Online, Springer, 2023, pp. 95–111.
[5] A. Alkan, Artificial intelligence: Its role and potential in education, İnsan ve Toplum Bilimleri
Araştırmaları Dergisi 13 (2024) 483–497.
[6] I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, V. Dimitrova, Artificial intelligence in education,
Springer, 2021.
[7] R. A. Abdulmunem, Artificial intelligence in education, in: Comparative Research on Diversity in
Virtual Learning: Eastern vs. Western Perspectives, IGI Global, 2023, pp. 241–255.
[8] G. Casalino, L. Grilli, P. Limone, D. Santoro, D. Schicchi, et al., Deep learning for knowledge
tracing in learning analytics: an overview., TeleXbe (2021).
[9] D. Taibi, G. Fulantelli, V. Monteleone, D. Schicchi, L. Scifo, et al., An innovative platform to
promote social media literacy in school contexts, in: ECEL 2021 20th European Conference on
e-Learning, Academic Conferences International Limited Berlin, 2021, p. 460.
[10] E. E. Week), Official website, https://www.edweek.org, n.d. Accessed: 2024-10-04.
[11] H. E. School, Official website, https://teach.extension.harvard.edu, n.d. Accessed: 2024-10-04.
[12] K. Dive, Official website, https://www.k12dive.com, n.d. Accessed: 2024-10-04.
[13] D. Baidoo-Anu, L. O. Ansah, Education in the era of generative artificial intelligence (ai): Under-
standing the potential benefits of chatgpt in promoting teaching and learning, Journal of AI 7
(2023) 52–62.
[14] H. Lee, Y. Hwang, Technology-enhanced education through vr-making and metaverse-linking to
foster teacher readiness and sustainable learning, Sustainability 14 (2022) 4786.
[15] T. Xu, Q. Gao, X. Ge, J. Lu, The relationship between social media and professional learning from
the perspective of pre-service teachers: A survey, Education and Information Technologies 29
(2024) 2067–2092.
[16] A. A. Gaafar, Metaverse in architectural heritage documentation & education, Advances in
Ecological and Environmental Research 6 (2021) 66–86.
[17] L. Kohnke, B. L. Moorhouse, D. Zou, Exploring generative artificial intelligence preparedness
among university language instructors: A case study, Computers and Education: Artificial
Intelligence 5 (2023) 100156.
[18] H. Vartiainen, M. Tedre, Using artificial intelligence in craft education: crafting with text-to-image
generative models, Digital Creativity 34 (2023) 1–21.
[19] A. R. Doshi, O. Hauser, Generative artificial intelligence enhances creativity, Available at SSRN
(2023).
[20] C. Zhang, C. Zhang, S. Zheng, M. Zhang, M. Qamar, S.-H. Bae, I. S. Kweon, A survey on audio
diffusion models: Text to speech synthesis and enhancement in generative ai, arXiv preprint
arXiv:2303.13336 (2023).
[21] M. Hueso, R. Álvarez, D. Marí, V. Ribas-Ripoll, K. Lekadir, A. Vellido, Is generative artificial
intelligence the next step toward a personalized hemodialysis?, Revista de investigación clínica 75
(2023) 309–317.
[22] A. G. Dunn, I. Shih, J. Ayre, H. Spallek, What generative ai means for trust in health communications,
Journal of Communication in Healthcare 16 (2023) 385–388.
[23] A. Hanai, T. Ishikawa, S. Kawauchi, Y. Iida, E. Kawakami, Generative artificial intelligence
for clinical communication: Implications for non-pharmacological interventions in health care.,
medRxiv (2023) 2023–09.
[24] D. H. Spennemann, Chatgpt and the generation of digitally born “knowledge”: How does a
generative ai language model interpret cultural heritage values?, Knowledge 3 (2023) 480–512.
[25] N. Rane, Role and challenges of chatgpt and similar generative artificial intelligence in arts and
humanities, Available at SSRN 4603208 (2023).
[26] E. M. Zucker, M. Makhortykh, R. Ulloa, D. Bultmann, D. J. Simon, Ai and archives: How can
technology help preserve holocaust heritage under the risk of disappearance?, Eastern European
Holocaust Studies 1 (2023) 357–363.
[27] B. J. Hess, N. Cupido, S. Ross, B. Kvern, Becoming adaptive experts in an era of rapid advances in
generative artificial intelligence, Medical Teacher 46 (2024) 300–303.
[28] D. Schicchi, D. Taibi, Ai-driven inclusion: Exploring automatic text simplification and complex-
ity evaluation for enhanced educational accessibility, in: International Conference on Higher
Education Learning Methodologies and Technologies Online, Springer, 2023, pp. 359–371.