=Paper=
{{Paper
|id=Vol-3879/AIxEDU2024_paper_24
|storemode=property
|title=Co-Designing Media Education Strategies: A Workshop on AI and Information Literacy
|pdfUrl=https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_24.pdf
|volume=Vol-3879
|authors=Maria Ranieri,Stefano Cuomo,Gabriele Biagini
|dblpUrl=https://dblp.org/rec/conf/aixedu/RanieriCB24
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==Co-Designing Media Education Strategies: A Workshop on AI and Information Literacy==
https://ceur-ws.org/Vol-3879/AIxEDU2024_paper_24.pdf
Co-Designing Media Education Strategies A Workshop on AI
and Information Literacy
Maria Ranieri1, Stefano Cuomo1 and Gabriele Biagini1†
1 University of Florence, Florence, Italy
Abstract
This article discusses a pilot workshop on the impact of Artificial Intelligence on the media and
information system, conducted as part of the European project "TADAM: Tools and Awareness about
(Dis)information, Algorithms, and Media." Designed as part of a two-and-a-half-day of the MED Summer
School (associazione Italiana di Media Education) on Artificial Intelligence and Media Education, the
workshop gathered 20 participants—primarily educators and PhD students— and focused on Generative
Artificial Intelligence (GAI), fake news, and media education. The goal was to foster a critical
understanding of GAI tools in the media and information production landscape, promote the ability to
consciously generate content using GAI, and facilitate the transfer of this experience to both school
settings and active, informed citizenship. The workshop adopted David Merrill's principles of
instructional design, focusing on "learning by doing" and co-design, where participants were actively
involved in practical tasks and problem-solving activities. This methodological approach emphasized
media production and reflection, fostering design thinking and strategic reflection on the role of AI in
media and educational contexts. The main objective was to collaboratively articulate media education
strategies, including best practices around the use of algorithms and AI in media environments.
Participants engaged in a series of activities that combined hands-on media production with critical
analysis, resulting in the co-design of a set of operational guidelines. These guidelines, aimed at
enhancing the understanding and use of AI tools in media education, provided practical strategies for
application in diverse educational settings. Emphasizing metacognition and reflection, the workshop
encouraged participants to critically assess AI's influence on media for fostering informed and active
citizenship. The workshop concluded with a plenary session to create guidelines for Expert prompter.
Through this experience, participants developed a deeper understanding of the challenges AI poses to
information and the tools needed to address them in an educational context.
Keywords
Media education, Fake News, Generative Artificial Intelligence, Instructional design, learning by doing1
1. Introduction
1.1. Fake news, misinformation, or disinformation?
The rapid growth of Artificial Intelligence (AI), particularly Generative AI (GenAI), has sparked
significant debate over its potential impacts on various sectors, including the media and information
landscape. Since the launch of tools like ChatGPT-3.5 in late 2022, the capabilities of GenAI have
drawn both enthusiasm for their potential benefits and concern about their potential harms [1,2].
Some experts have even raised alarms about existential risks posed by advanced AI technologies
[3,4]. However, a more immediate and pressing concern is the potential negative impact of GenAI
on news consumption and production, as many media outlets have already integrated GenAI into
their processes by mid-2023. The ability of GenAI to create compelling narratives and content raises
new challenges in distinguishing between high-quality news and deceptive content, such as fake
AIxEDU – 2nd International Workshop on Artificial Intelligence System in Education – 25-28 November 2024, Bolzano,
Italy
⋆ Corresponding author.
maria.ranieri@unifi.it (M.Ranieri); stefano.cuomo@unifi.it (S.Cuomo); gabriele.biagini@unifi.it (G.Biagini)
0000-0002-8080-5436 (M.Ranieri); 0000-0003-3174-7337 (S.Cuomo); 0000-0002-6203-122X (G.Biagini)
© 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
news or tabloid-like clickbait. These developments suggest that GenAI may inadvertently amplify
the attractiveness of misleading information, shifting consumer attention away from reliable news
sources toward deceptive content.
Fake news, often understood as fabricated information created to mislead, has become an
increasingly pervasive issue in the digital age. Disinformation, a subset of fake news, involves the
deliberate dissemination of false information with the intent to deceive, while misinformation refers
to the spread of false information believed to be true by the sender [5,6,7]. The widespread
propagation of fake news, particularly through social media channels such as Facebook, Twitter,
YouTube, and WhatsApp, has transformed the landscape of information dissemination over the past
decade [8,9,10]. This transformation has led to a surge in misleading or false content that can incite
violence, manipulate public opinion, and even influence elections, highlighting the urgent need for
effective detection and countermeasures against such disinformation.
Within this context, research on the intersection of generative AI and fake news is critical, as
advanced AI algorithms are increasingly utilized both to create and to detect deceptive content [11].
Understanding how generative AI can be used to craft convincing fake narratives is vital for
developing more effective counter-strategies and assessing the ethical implications of AI-driven
misinformation.
Recognizing these challenges, the TADAM project ("Tools and Awareness about
(Dis)information, Algorithms, and Media") was initiated by a consortium of nine European partners
to foster a bottom-up approach in enhancing media literacy across Europe. The project aims to
exchange media literacy policies, collect good practices, and co-design innovative educational
resources that address the issues of algorithms and AI in the media and information system. Through
an initial European seminar and a series of regional and cross-sectoral workshops, TADAM seeks to
engage diverse stakeholders—including researchers, media professionals, educators, and IT
platforms—in developing adaptive strategies to mitigate the impact of AI-enabled disinformation.
The current study, part of this broader initiative, explores the effectiveness of one such workshop in
building capacity for media information literacy (MIL) related to AI.
1.2. The Role of the Critical Prompter in Combating Misinformation
In the context of combating misinformation, the concept of the "critical prompter" emerges as a vital
countermeasure. Critical prompting, fundamentally, involves the strategic crafting of inputs to elicit
desired responses or behaviours from AI systems. Within educational settings, this translates into
designing prompts that not only engage learners but also challenge them to think critically and
creatively. By leveraging critical prompting, AI can be transformed from a passive repository of
information into an interactive tool that fosters deeper learning and understanding [12].
The relevance of critical prompting in education is increasingly significant as AI tools become
more sophisticated and integrated into learning environments. Effective prompting empowers
educators to guide AI interactions in ways that enhance the educational experience, create tailored
learning scenarios, and adapt to the diverse needs and abilities of individual students, making
learning more engaging and impactful [13]. This approach not only deepens students' understanding
of subject matter but also sharpens their critical thinking skills—an essential competency in a fast-
paced, ever-evolving world.
As studies have shown, teaching students how to effectively prompt AI systems can help them
recognize the limitations of these technologies, thereby fostering AI literacy [14]. Moreover,
integrating critical prompting into education has the potential to create highly interactive and
playful learning environments, particularly when combined with game-based learning strategies
[15].
In the field of prompting, methodologies such as Zero-Shot and Few-Shot prompting have
emerged as effective techniques. Zero-Shot prompting involves providing a simple, straightforward
input to an AI model without extensive details, useful for broad or general problems. Conversely,
Few-Shot prompting enriches the input with multiple examples, guiding the AI more precisely in
complex queries where specific ideas or data are available. These techniques exemplify the
experimental and evolving nature of critical prompting, where continuous refinement of prompts
leads to improved outputs and learning experiences [16].
Given the relevance of critical prompting for media and information literacy to contrast
misinformation, this workshop aimed promote participants’ critical prompting skills through the
development of tools and methodologies designed to guide AI interactions in a way that supports
accurate information dissemination and critical analysis. With this in mind the current study
intended to address two key research questions:
RQ 1: What is the perceived relevance of the impact of GenAI on the media and information among
participants?
RQ 2: What is the perceived effectiveness of the workshop’s methodological approach in building
capacity for media information literacy (MIL)?
2. Methodology
2.1. The workshop and the participants
The workshop was held as part of the XXXIII Summer School of MED – the Italian Association for
Media Education, in Modena, Italy, from 16 to 18 July 2024. Designed to address teachers,
pedagogists, media educators, and PhD students, it was centered on the theme of artificial
intelligence in the landscape of media literacy education [16,17,18,19,20,21] . The aim was to explore
a classic media education topic such as fake news through the lens of the opportunities and
challenges presented by Generative Artificial Intelligence (GAI).
Conducted over two and a half days, the workshop was embedded within a broader Summer
School on Artificial Intelligence and Media Education, which featured 11 hours of workshops,
alongside keynote speeches and presentations of various projects. This setting allowed for
substantial experimentation with methodologies, which could be applied in subsequent Regional
Workshops. The workshop aimed to achieve three primary objectives: fostering a critical
understanding of GAI tools within the media and information production landscape, promoting the
ability to consciously generate content using GAI, and facilitating the transferability of these skills
to both educational settings and active, informed citizenship. Approximately 20 participants attended
the workshop, with an average age of 35 (SD=4). The group included individuals with a background
in teaching and media education, as well as several PhD students. All participants had a high level
of education (bachelor’s degree or higher) and a medium to good familiarity with Generative AI
tools. They also had prior experience in content creation using large language models (LLMs) and
image generation tools. The diverse yet specialized audience provided a dynamic environment for
exploring the intersections between AI, misinformation, and media education.
2.2. The research instrument
To assess the outcomes of the workshop and answer the research questions, a structured
questionnaire was administered to all participants at its conclusion. The questionnaire was
developed using Google Forms and consisted of 9 items, in addition to a section for demographic
information. The items were designed to measure two key areas: the perceived relevance of the
workshop topic (4 items focused on the impact of Generative AI on media and information) and the
perceived effectiveness of the workshop in enhancing media information literacy (MIL) capacity
building (5 items). Responses were recorded on a 5-point Likert scale, where 1 represented "not at
all" and 5 represented "a great deal." The questionnaire required approximately 15 minutes to
complete, and all 20 participants provided responses. Detailed analyses of the individual items are
presented in the results section.
2.3. The structure of the activities
To support the complex process of exploring the impact of Generative Artificial Intelligence (GAI)
on media and misinformation, a robust educational framework was developed, drawing from diverse
research traditions. The workshop was grounded in David Merrill's [22] principles of instructional
design, emphasizing practical, hands-on activities and real-world problem-solving to facilitate
learning. This framework was further enhanced by a "learning by doing" and co-design approach,
aimed at generating effective media education strategies related to AI and algorithms. Participants
were actively engaged in small-scale media production and reflection activities to stimulate design
thinking and strategic consideration of AI's role in the media and educational landscape. Given the
number of participants, four working groups were formed, each consisting of 4-5 members. The
workshop followed a structured methodology designed to progressively build understanding and
skills in the critical analysis and creation of media content in the context of GAI. The steps of the
methodology were as follows:
1. Icebreaking Activity (Activation: 30 minutes): Participants started with an icebreaking
activity where each person shared a true or false fact about themselves. This exercise was
intended to activate critical thinking and engagement among the participants.
2. Presentation of the Fake News Topic (Expert Documentation: 30 minutes): The workshop
continued with an introduction to the topic of fake news, providing participants with
foundational knowledge and context.
3. Online Search for Fake News (Personal Documentation: 1 hour): Each group conducted
an online search to identify a fake news item corresponding to an assigned category. This
activity allowed participants to explore different types of misleading information firsthand.
4. Generation of Fake News and Refinement of Prompting (Product Generation: 1.5 hours):
Participants then used GAI tools to generate their own fake news content, experimenting
with prompts and refining their approach to maximize the content's deceptive qualities.
5. Analysis of Productions (Product Analysis: 1.5 hours): The groups conducted a cross-
analysis of the fake news items created, using a predefined analysis grid to assess the
effectiveness and characteristics of the generated content.
6. Analysis of Generative Prompts (Process Analysis: 1.5 hours): Participants examined the
generative prompts used in the creation of fake news, applying specific labels to analyse
their effectiveness and the potential for manipulation.
7. Creation of Guidelines for "Critical Prompting" (Synthesis: 1.5 hours): Finally, each group
created five guidelines for "Critical Prompting" to enhance media analysis, drawing on the
insights gained from the workshop. These guidelines were designed for application in both
school contexts and active citizenship.
This structured approach provided participants with a comprehensive framework to critically
engage with the capabilities and challenges of GAI in the context of media and misinformation,
ultimately fostering a deeper understanding of how AI tools can be utilized responsibly in media
education.
All the guidelines were subsequently presented in a plenary session, providing an opportunity
for discussion and refinement. Additionally, participants' self-perception regarding their capacity-
building was evaluated through an ad hoc questionnaire administered at the end of the workshop.
Figure 1 Examples of students “fake news” production during the workshop activities.
2.4. The EAVI taxonomy
To explore the impact of Generative AI on the media landscape, our workshop began with an "expert
documentation" phase, allowing participants to delve into the topic of misleading news through a
detailed taxonomy provided and explained during the session. This taxonomy, developed by EAVI
(Media Literacy for Citizenship), categorizes ten distinct types of misleading news, each with unique
characteristics, motivations, and potential impacts on audiences.
• Propaganda: This type is often employed by governments, corporations, or non-profit
organizations to influence attitudes, values, and knowledge. Propaganda appeals to emotions
and can be either beneficial or harmful, depending on its intent. It has a high impact due to
its ability to manipulate public opinion for political power.
• Clickbait: Defined by eye-catching and sensational headlines designed to capture attention,
clickbait often misleads readers, with the actual content not necessarily reflecting the
headline. The primary motivation behind clickbait is generating ad revenue, and its impact
is rated medium due to its widespread use and potential to distort information.
• Sponsored Content: This is advertising disguised as editorial content, which can create a
conflict of interest for legitimate news organizations. Consumers may not recognize it as
advertising if it is not clearly labelled. Its motivation is monetary, and it is considered to have
a medium impact on public perception and trust in news.
• Satire and Hoax: Often intended as social commentary or humour, satire, and hoaxes vary
widely in quality and clarity. The intended meaning may not always be apparent, and they
can embarrass or mislead audiences who mistake them for factual news. They have a low
impact, mainly motivated by humour or fun.
• Error: Even established news organizations can make mistakes. These errors can damage
credibility, offend audiences, or result in legal consequences. Reputable organizations
typically issue corrections or apologies, and the impact is considered neutral.
• Partisan: Partisan news interprets facts in a way that aligns with specific ideologies while
claiming to be impartial. It privileges facts that support its narrative and often uses emotional
or passionate language. This type of misleading news has a medium impact and is motivated
by political or ideological goals.
• Conspiracy Theory: This type tries to explain complex realities as responses to fear or
uncertainty, often rejecting expert opinions and authoritative evidence. Conspiracy theories
are not falsifiable; attempts to refute them are often seen as further proof of the conspiracy.
They have a high impact and are motivated by misinformation.
• Pseudoscience: Pseudoscience includes false or exaggerated claims, such as miracle cures or
climate change denial, which often contradicts established scientific knowledge. These
claims are motivated by financial gain and can have a high impact due to their potential to
mislead public understanding of science.
• Misinformation: This category includes a mix of factual, false, or partially false content,
often created with the intent to inform, although the author may not know the information
is incorrect. Misinformation also encompasses false attributions, doctored content, and
misleading headlines. It has a medium impact and is motivated by misinformation.
• Bogus: Refers to entirely fabricated content designed to intentionally deceive. This can
include guerrilla marketing tactics, bot-generated comments, and counterfeit branding
efforts driven by political or financial motivations. Bogus content is highly impactful due to
its deliberate intent to mislead.
In addition to these ten types, the taxonomy also highlights several specific techniques used to
mislead, such as false attribution (authentic content misattributed to incorrect events or people),
counterfeit (imitation of reputable sources), misleading (discrepancies between content and its
representation), and doctored content (altered media like photos or videos).
By engaging with this taxonomy, participants gained a comprehensive understanding of the
various forms of misleading news, laying the groundwork for further exploration into the role of
Generative AI in the creation and propagation of such content. This foundation informed subsequent
workshop activities, where participants applied this knowledge to critically analyse and address the
challenges of AI-enabled misinformation.
3. Results
3.1. RQ 1: Perceived relevance of the impact of GenAI on the media and information
among participants?
The first research question (RQ1) aimed to assess the perceived relevance of the workshop topic,
which focused on the impact of Generative Artificial Intelligence (GAI) on misinformation and media
literacy, in various contexts. The participants were asked to rate the relevance of the topic for four
key areas: educational contexts, local needs, potential stakeholders, and their professional
background. The responses were gathered on a scale from 1 (not relevant) to 5 (highly relevant).
The data show a strong perceived relevance of the topic for educational contexts, with 14 out of 20
participants rating it as highly relevant (5). Six participants rated it as fairly relevant (4), indicating
broad agreement on the importance of the topic for educational settings. The average relevance score
for educational contexts was 4.65 (SD = 0.48).
In terms of local needs, participants expressed varied opinions. While 11 participants rated the
topic as highly relevant (5), five participants gave a score of 4, and four participants rated it at a level
of 3. The average score for local needs was 4.3 (SD = 0.56).
The relevance of the workshop topic for potential stakeholders, such as educational institutions,
policymakers, and community organizations, received high ratings overall. Thirteen participants
rated the topic as highly relevant (5), while six rated it as fairly relevant (4). Only one participant
gave a score of 3. The average score for this category was 4.6 (SD = 0.49).
When considering the relevance of the topic to their professional background, participants also
showed a generally positive response. Eleven participants rated the topic as highly relevant (5), six
participants rated it as fairly relevant (4), and three rated it at a level of 3. The average score was 4.25
(SD = 0.62). The following table (Table 1) summarises the results.
Table 1
Items, frequency, mean and standard deviation for “Perceived relevance of the impact of GenAI on
the media and information.”
Item Results Mean SD
Relevance for Educational 14 out of 20 rated 5; 6 rated 4 4,65 0,48
Contexts
Relevance for Local Needs 11 out of 20 rated 5; 5 rated 4; 4,3 0,56
4 rated 3
Relevance for Potential 13 out of 20 rated 5; 6 rated 4; 4,6 0,49
Stakeholders 1 rated 3
Relevance for Professional 11 out of 20 rated 5; 6 rated 4; 4,25 0,62
Background 3 rated 3
3.2. RQ 2: Perceived effectiveness of the workshop on MIL capacity-building?
The second research question (RQ2) focused on evaluating the perceived effectiveness of the
workshop in building capacity for media information literacy (MIL) related to AI. Participants were
asked to rate their preparedness in several areas following the workshop, including promoting MIL
skills around AI, increasing local knowledge about the workshop topic, facilitating training activities,
fostering networking among stakeholders, and their interest in testing the workshop methodology
in their professional contexts. Responses were rated on a scale from 1 (not at all prepared) to 5 (very
well prepared).
Participants felt generally well-prepared to promote MIL skills related to AI following the
workshop. Out of 20 responses, 11 participants rated their preparedness as a "5" (very well prepared),
while 9 participants rated it as a "4". The average score for this item was 4.62 (SD = 0.49).
Regarding the ability to increase local knowledge on the workshop topic, participants again
expressed strong confidence. Ten participants rated their preparedness as "5", while ten rated it as
"4". The mean score for this item was 4.57 (SD = 0.49).
When asked about their preparedness to facilitate training activities on the workshop topic, the
responses were more varied. Nine participants rated their preparedness as "5", eight participants
rated it as "4", and three participants rated it as "3". The mean score was 4.48 (SD = 0.59).
The perceived effectiveness in fostering networking among stakeholders showed the greatest
variation in responses. Seven participants rated their preparedness as "5", eight participants rated it
as "4", and five participants rated it as "3". The mean score for this item was 4.29 (SD = 0.70).
Most participants expressed a strong interest in applying the workshop methodology in their
professional contexts. Out of 20 responses, eight participants rated their interest as "5", eight rated it
as "4", and four rated it as "3". The average score was 4.24 (SD = 0.75). The following table (Table 2)
summarises the results.
Table 2
Items, frequency, mean and standard deviation for “Perceived effectiveness of the workshop on MIL
capacity-building”
Item Results Mean SD
Preparedness to Promote MIL Skills 11 rated 5; 9 rated 4 4,62 0,49
Around AI
Preparedness to Increase Local Knowledge 10 rated 5; 10 rated 4 4,57 0,49
Preparedness to Facilitate Training 9 rated 5; 8 rated 4; 4,48 0,59
Activities 3 rated 3
Preparedness to Foster Networking 7 rated 5; 8 rated 4; 4,29 0,7
Among Stakeholders 5 rated 3
Interest in Testing Methodology in 8 rated 5; 8 rated 4; 4,24 0,75
Professional Context 4 rated 3
4. Discussion
The findings of this study highlight the significant relevance and perceived effectiveness of the
workshop in addressing the challenges posed by Generative AI (GenAI) in the media and information
landscape. The high relevance scores across various dimensions—educational contexts, local needs,
and stakeholder engagement—underscore the pressing concern about the impact of AI-driven
misinformation. As noted in the introduction, the rapid proliferation of GenAI tools, such as
ChatGPT-3.5, has amplified the potential for generating misleading information [1,2]. Participants
in this study consistently rated the topic of the workshop as highly relevant, with particular emphasis
on its importance for educational settings and for potential stakeholders. This finding aligns with
the growing awareness of the role that educational institutions and policymakers must play in
countering the spread of disinformation, a concern echoed by researchers like Kim and Dennis [5]
and Reisach [7].
The workshop's perceived effectiveness in building capacity for media information literacy (MIL)
also reflects a broader recognition of the need for practical skills to combat the spread of fake news
and disinformation. The participants' preparedness to promote MIL skills around AI and to increase
local knowledge suggests that the workshop successfully equipped them with the tools to address
these challenges in their respective contexts. This is particularly important given the ability of GenAI
to create compelling and deceptive narratives that can manipulate public opinion, as highlighted in
previous studies [6,11].
However, the variability in participants' confidence in facilitating training activities and fostering
networking among stakeholders indicates areas where additional support or resources may be
needed. This finding suggests that while the workshop was effective in raising awareness and
enhancing critical understanding, there is still a need for further development of practical strategies
for engaging diverse stakeholders and conducting training sessions. As noted by Zafarani et al. [9],
the complexity of misinformation and the rapid evolution of AI technologies require a dynamic
approach that includes ongoing education, collaboration, and the development of adaptive strategies.
The interest in applying the workshop methodology in professional contexts reflects a willingness
among participants to experiment with innovative approaches to MIL, yet also points to potential
barriers, such as a lack of resources or support, which could limit the effectiveness of such efforts.
This aligns with the broader challenge identified by Hunt and Gentzkow [10] in ensuring that
educational interventions remain accessible and sustainable in diverse settings.
Overall, these findings suggest that while the workshop was effective in building capacity and
raising awareness about the impact of GenAI on misinformation, further efforts are needed to
strengthen practical skills and facilitate wider implementation of MIL strategies. Future research
should focus on developing tailored interventions that address specific local needs and stakeholder
dynamics, as well as exploring the ethical implications of using AI-driven tools in media education
[8,11].
5. Limitations
This study has several limitations that should be considered when interpreting the findings. Firstly,
it is important to emphasize that these conclusions cannot be applied uniformly given the
characteristics of the participants (i.e.., a convenience sample, therefore neither probabilistic nor
representative of the reference population). The participants were predominantly educators, media
professionals, and PhD students with prior experience in media education and familiarity with AI
tools. Therefore, the findings may not fully represent the broader population of educators or
stakeholders who have less familiarity with Generative AI (GenAI) or media literacy concepts.
Secondly, the study relied on self-reported data collected through a questionnaire, which may be
subject to response biases, such as social desirability bias, where participants might have rated the
workshop more positively due to perceived expectations or a desire to align with group norms.
Additionally, the questionnaire did not explore deeper qualitative insights into the participants'
experiences, which could have provided a more nuanced understanding of the challenges and
opportunities presented by the workshop methodology.
Thirdly, the workshop's effectiveness in building media information literacy (MIL) capacity was
assessed immediately after the intervention. This approach does not account for the long-term
retention of skills and knowledge gained or their practical application in professional settings. Future
studies could incorporate follow-up assessments to evaluate the sustained impact of the workshop
over time.
Finally, the study did not explore the specific contextual factors that might affect the applicability
of the workshop methodology across different regions or educational settings. Factors such as local
media environments, resource availability, and varying levels of digital literacy among stakeholders
could significantly influence the perceived relevance and effectiveness of similar workshops.
Future research should address these limitations by expanding the sample size, incorporating a
broader range of participants, using mixed-method approaches to gather both quantitative and
qualitative data, and conducting longitudinal studies to assess the long-term impact of MIL capacity-
building interventions.
6. Conclusion
This study explored the perceived relevance and effectiveness of a workshop on media information
literacy (MIL) in the context of Generative AI (GenAI) and misinformation. The findings indicate
that participants found the workshop topic highly relevant across multiple contexts, including
educational settings, local needs, and among potential stakeholders.
The workshop was also perceived as effective in building participants' capacity to promote MIL skills
related to AI, increase local knowledge, and apply the learning in their professional contexts.
However, there remains a need for further support in facilitating training activities and fostering
stakeholder networking, highlighting areas for improvement in future workshops. The study
suggests that incorporating more practical strategies and resources could enhance the workshop's
impact and better prepare participants to combat the spread of misinformation.
Future research should continue to investigate the role of educational interventions in countering
AI-driven disinformation and explore new methodologies to enhance MIL capacity-building.
Additionally, as AI technologies evolve, ongoing evaluation of their ethical implications and their
effects on the media landscape will be crucial to developing adaptive and effective counter-strategies
[1,2,11].
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