AI Wanderlust: A Roadmap to Integrating GenAI Tools in the

Classroom Fostering Critical AI Literacy

Lisa Kuka

, Corinna H

ormann

, Marina Unterweger

and Barbara Sabitzer

STEM Didactics, Johannes Kepler University, Altenberger Straße 68, 4040 Linz, Austria

Keywords:

Artiﬁcial Intelligence, K12 Education, AI Literacy, GenAI, Project-Based Learning, Critical Thinking, Ethics.

Abstract:

Embarking on an innovative educational journey, this paper delves into the dynamic integration of Generative

AI (GenAI) tools into high school education centering around the implementation of a practical project, ”AI

Wanderlust.” This project goes beyond imparting technical skills; it should serve as a catalyst for instilling a

reﬂective mindset in students. The core task involves students creating an immersive virtual travel experience

using GenAI-generated content, aiming to foster creativity, critical thinking, and AI literacy. The overarch-

ing objective is to explore the ethical implications associated with GenAI integration within an educational

context. Aligned with a roadmap for lesson design to foster critical AI Literacy, this project seeks to develop

essential skills crucial in the ever-evolving landscape of AI in education. By extending beyond technical pro-

ﬁciency, the project emphasizes teacher-guided ethical reﬂection. This combination of hands-on engagement

and mentorship ensures that students not only learn to use GenAI tools effectively but also develop a nuanced

understanding of the ethical considerations inherent in the ﬁeld. In essence, the project and its associated

roadmap represent a proactive approach to propel high school education into the era of Generative AI. The

aim is to cultivate students not merely as users but as informed, critical thinkers equipped with the skills and

ethical awareness needed to navigate the multifaceted landscape of GenAI responsibly and thoughtfully in the

broader context of Artiﬁcial Intelligence education.

1 INTRODUCTION

In the ever-evolving landscape of Artiﬁcial Intelli-

gence (AI), the emergence of OpenAI’s ChatGPT in

November 2022 has ignited a wave of curiosity and

exploration. With a rapid surge in user numbers at-

taining one million users within a week of its launch

(Polymer, 2023), its widespread acceptance is evi-

dent, owing to its user-friendly interface and per-

ceived high usefulness (Leiter et al., 2023; Xu et al.,

2023; Karakose, 2023). This paper navigates the in-

triguing intersection of educational technology and

ethical concerns surrounding Generative AI (GenAI)

such as ChatGPT. As students increasingly incorpo-

rate GenAI into their daily lives (Forman et al., 2023;

Chan and Lee, 2023), it becomes imperative for class-

rooms to not only teach the proper usage of GenAI,

emphasizing prompt engineering, but also to instill a

reﬂective mindset. Issues ranging from the potential

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https://orcid.org/0000-0001-5772-0672

https://orcid.org/0000-0002-1304-6863

for hallucinations within language models to the risk

of misuse, such as plagiarism, underscore the need for

careful reﬂection. Additionally, students should learn

to reﬂect the outcome of GenAI tools and their pos-

sible impact on society including ethical questions.

There is a growing awareness of the potential biases

and stereotypical outcomes that might emerge from

the use of GenAI.

Under the guiding principle of ”Teach GenAI with

GenAI”, this paper proposes a roadmap for a project-

based lesson design titled ”AI Wanderlust.” It initi-

ates by clarifying essential AI terms, progresses to

outline a robust roadmap, introduces the captivating

task titled ”AI Wanderlust,” and meticulously deﬁnes

educational objectives, aligning with Bloom’s taxon-

omy Bloom (1956). Furthermore, the paper offers ad-

vice and strategies to guide students toward critical

thinking and addresses general ethical concerns sur-

rounding the integration of ChatGPT in educational

contexts. This paper embarks on a journey to equip

students with the skills needed to navigate the realm

of GenAI responsibly and thoughtfully.

Kuka, L., Hörmann, C., Unterweger, M. and Sabitzer, B.

AI Wanderlust: A Roadmap to Integrating GenAI Tools in the Classroom Fostering Critical AI Literacy.

DOI: 10.5220/0013357100003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 521-528

ISBN: 978-989-758-746-7; ISSN: 2184-5026

521

2 BACKGROUND

2.1 Understanding the Hierarchical

Landscape of AI

2.1.1 AI (Artiﬁcial Intelligence)

Artiﬁcial Intelligence (AI) stands as the overarching

concept that encompasses the development of com-

puters and robots capable of performing tasks that

typically require human intelligence. It serves as the

foundation for various specialized domains, includ-

ing Machine Learning, Deep Learning, and Genera-

tive AI.

Figure 1: Hierarchical Landscape of AI.

2.1.2 ML (Machine Learning)

Within the realm of AI, Machine Learning (ML)

emerges as a pivotal discipline, emphasizing the abil-

ity of algorithms to learn and improve from experi-

ence. Machine Learning can be broadly categorized

into three main paradigms: In Supervised Learn-

ing, algorithms are trained on labeled datasets, where

the model learns to map inputs to corresponding out-

puts, facilitating tasks like image classiﬁcation. Un-

supervised Learning involves algorithms working

with unlabeled data, discovering patterns and rela-

tionships within the data, as seen in clustering algo-

rithms, whereas Reinforcement Learning focuses on

learning through interaction with an environment, re-

ceiving feedback in the form of rewards or penalties.

This paradigm is applied in scenarios like game play-

ing and robotic control.

2.1.3 Deep Learning:

Deep Learning, a subset of Machine Learning, lever-

ages neural networks with multiple layers to achieve

complex data analysis. Within the landscape of Deep

Learning, various architectures play distinctive roles:

Large Language Models (LLMs), such as GPT-3,

are advanced language models designed for natural

language understanding and generation, showcasing

the potential of deep neural networks in processing

and generating textual information. Generative Ad-

versarial Networks (GANs) introduce a novel ap-

proach to generative tasks, utilizing the interplay be-

tween a generator and a discriminator. GANs have

been particularly successful in text-to-text and text-

to-image generation, demonstrating their prowess in

creative content creation.

2.1.4 Special Role of Generative AI (GenAI)

Generative AI occupies a distinctive space within the

AI landscape, focusing on systems capable of produc-

ing new content. Large Language Models (LLMs)

and Generative Adversarial Networks (GANs) stand

out as pioneers in this ﬁeld. In text-to-text and text-to-

image generation, the combined capabilities of LLMs

and GANs have led to breakthroughs, showcasing the

potential of Generative AI in creative content genera-

tion.

The relationships between Artiﬁcial Intelligence,

Machine Learning, Deep Learning, and Generative AI

are hierarchical and complementary. As can be seen

in Figure 1, AI provides the overarching goal, while

Machine Learning serves as a critical methodology

within AI. Deep Learning, with its various architec-

tures like LLMs, GANs, CNNs, RNNs, and LSTMs,

represents a sophisticated approach within Machine

Learning, offering solutions to complex data analysis.

2.2 Adapting Baacke’s Media

Competence Model as an AI

Literacy Framework

Baacke’s Media Competence Model (1996) offers

a foundational framework for integrating digital

literacy and ethical considerations into education.

By emphasizing four dimensions—media criti-

cism, media knowledge, media usage, and media

design—the model provides a robust structure for

fostering the skills and understanding necessary

to navigate the digital world Baacke (1996). This

framework can be effectively adapted to address

the speciﬁc competencies required for AI literacy,

focusing on the critical, practical, and ethical engage-

CSEDU 2025 - 17th International Conference on Computer Supported Education

522

Figure 2: AI Literacy based on Baacke (Kuka et al. (2024)).

ment with AI technologies, as can be seen in Figure 2.

1. AI Criticism. AI criticism, akin to media criti-

cism, focuses on analyzing AI-generated content

for biases, stereotypes, inaccuracies, and ethical

concerns. By assessing the strengths and limita-

tions of AI tools, students develop critical think-

ing skills and become more aware of the societal

implications of AI.

2. AI Knowledge. AI knowledge involves under-

standing the functionality of AI systems, includ-

ing machine learning and natural language pro-

cessing. Students learn about prompt engineering

and the role of datasets, gaining hands-on expe-

rience with tools like ChatGPT and text-to-image

generators to reinforce their understanding.

3. AI Usage. Practical Application of AI for

Problem-Solving AI usage reﬂects the practical

component of media usage, emphasizing the ap-

plication of AI tools to address real-world chal-

lenges. Learners engage in activities that in-

volve generating content, solving problems, or en-

hancing workﬂows with AI technologies, allow-

ing them to develop hands-on experience and ap-

ply AI solutions creatively and effectively.

4. AI Design. Creating Ethical and Responsible

AI Applications AI design extends the dimension

of media design by focusing on the creation of

responsible and ethically sound AI applications.

This involves integrating considerations such as

fairness, transparency, and accountability into the

design and deployment of AI tools. Learners are

encouraged to reﬂect on the broader impact of

their design choices, ensuring that AI technolo-

gies are used thoughtfully and responsibly.

By adapting Baacke’s Media Competence Model to

the context of AI literacy, educators can provide

a comprehensive framework that balances technical

proﬁciency with critical reﬂection and ethical respon-

sibility. This approach ensures learners are equipped

to navigate the complex landscape of AI technologies

with conﬁdence and an understanding of their societal

impact.

3 ROADMAP FOR LESSON

DESIGN TO FOSTER

CRITICAL AI LITERACY

Exploring the nuances of lesson design for a project-

based assignment that aims to build critical AI liter-

acy, this segment uncovers a roadmap guided by the

principle ”Teach GenAI with Gen AI.” As can be seen

in Figure 3, the outlined steps encompass the entire

AI Wanderlust: A Roadmap to Integrating GenAI Tools in the Classroom Fostering Critical AI Literacy

523

pedagogical journey, beginning with the inspiration

phase, where diverse and engaging topics are discov-

ered and selected to stimulate student interest. The

creation phase involves GenAI Story Weaving, utiliz-

ing ChatGPT for crafting compelling narratives that

explicitly encourage critical thinking. The illustration

phase emphasizes the incorporation of text-to-image

GenAI, such as Ideogram, to enhance visuals, cou-

pled with a conscientious consideration of ethical im-

plications. The showcase phase advises selecting an

interactive platform that prioritizes critical engage-

ment. The engagement phase calls for the integra-

tion of quizzes, challenges, and decision-making ele-

ments to foster active participation while also encour-

aging ethical reﬂections. Notably, the paper under-

scores the pivotal role of critical and ethical reﬂec-

tion throughout the entire process, with an emphasis

on the teacher’s guidance. Recognizing that teachers

must possess a nuanced understanding of GenAI me-

chanics, ethical considerations, and effective teaching

strategies, the subsequent subsection provides valu-

able insights and advice on cultivating critical think-

ing in the classroom.

3.1 Project Description

Based on the roadmap this section will provide an ex-

ample task for educators and students alike, foster-

ing a deep understanding of the multifaceted nature

of integrating Generative AI into Upper Secondary

projects.

The following description is designed for direct

distribution to students, with the option to include the

detailed task description if desired. While the task

details are available, they are not obligatory to hand

out by teachers, allowing for ﬂexibility. This descrip-

tion, tailored for direct dissemination to students, em-

braces the ﬂexibility to include a detailed task de-

scription, emphasizing the objective of engaging with

Generative AI tools. Acknowledging the potential in-

ﬂuence of vague instructions on creativity, recent re-

search offers nuanced insights. Dove et al. (2017)

suggest that constraints and ambiguity can positively

support small-scale creativity. Simultaneously, Mas-

cio et al.’s (2018) ﬁndings underscore the importance

of the wording and placement of task instructions in

shaping the novelty and workability of ideas. Lev-

enson’s (2013) emphasis on task features, cognitive

demands, emotions, and values aligns with the notion

that these factors play pivotal roles in promoting cre-

ativity, especially in mathematical contexts. Never-

theless, Halpern’s (2010) cautionary note about a gap

between the potential for enhancing creativity and ac-

tual practices in university classrooms adds a layer

Figure 3: Roadmap: Lesson Design to Foster Critical AI

Literacy.

of complexity to the understanding. In light of these

diverse perspectives, the deliberate vagueness in task

descriptions is proposed as a means to evoke creative

thinking, recognizing that the impact may vary based

on the context and the nature of the task. However,

in this scenario, the vague description could lead to a

more creative outcome and a deeper examination of

the topic.

Project Title. AI Wanderlust: Exploring Destinations

Virtually with GenAI

Objective. Create an immersive virtual travel ex-

perience using AI-generated content to guide users

through various destinations. This project aims to

combine the creativity of students with the capabil-

CSEDU 2025 - 17th International Conference on Computer Supported Education

524

ities of GenAI tools like ChatGPT and Ideogram to

offer a unique and engaging exploration of different

places.

Task Description.

1. Destination Selection:

• Choose a topic for your travel guide.

• Choose a list of diverse destinations for the vir-

tual travel experience. These could include his-

torical sites, natural wonders, or culturally rich

cities.

2. AI-Generated Content:

Use ChatGPT to create travel guides, stories, di-

alogues, and interesting facts about each destina-

tion. Ensure that the AI-generated content adds

value to the virtual travel experience.

3. Visual Enhancements:

Utilize a text-to-image GenAI (Ideogram, Bing

Image Creator, Adobe Fireﬂy, . . . ) to visually rep-

resent the unique aspects of each destination. This

could include creating visual symbols, maps, or il-

lustrations to enhance the overall experience.

4. Interactive Platform:

Select a platform for presenting the virtual travel

experience. This could be a website, app, or inter-

active presentation. Consider incorporating multi-

media elements like audio, video, and interactive

features.

5. User Engagement:

Design interactive elements that allow users to

engage with the virtual travel experience. This

might include quizzes, challenges, or decision-

making scenarios based on AI-generated content.

6. Critical Reﬂection:

Throughout the project, you should critically re-

ﬂect on the use of AI in shaping the virtual travel

experience. Consider questions such as:

• How does AI contribute to the storytelling and

engagement of users?

• Are there potential biases in the AI-generated

content, and how can these be addressed?

• What ethical considerations should be taken

into account when using AI in this context?

7. Presentation:

You will present your virtual travel experience to

the class. Emphasize the importance of effective

communication and user experience in your pre-

sentation.

8. Peer Evaluation:

Encourage your classmates to evaluate and pro-

vide constructive feedback on your projects. Con-

sider aspects like creativity, usability, and the in-

corporation of AI-generated content.

3.2 Educational Objectives

The project’s objectives are twofold, aiming to pro-

vide students with a multifaceted understanding of

GenAI tools. Firstly, students will explore text-to-text

tools, including platforms like ChatGPT and Poe,

alongside text-to-image tools such as Ideogram and

Bing Image Creator. Through hands-on experiences,

they will develop proﬁciency in prompt engineering,

honing their skills to effectively guide AI models

and tailor outputs to project-speciﬁc requirements.

Simultaneously, the second objective underscores

the critical need for students to engage in reﬂective

and nuanced discussions about the outputs of GenAI

tools, particularly when dealing with sensitive topics

like culture. Recognizing the potential biases embed-

ded in AI-generated content, the process is carefully

guided by a teacher, fostering an environment where

students can explore, question, and heighten their

awareness of the ethical implications inherent in

utilizing AI. The overarching awareness of AI’s

capacity to cement bias underscores the importance

of a guided, teacher-led approach to ensure respon-

sible and thoughtful integration of Generative AI in

educational projects.

Guided by Bloom’s Taxonomy (Bloom, 1956), the

tasks navigate through the various levels of cognitive

skills, the objectives are strategically outlined to

facilitate a comprehensive learning experience for

students. The objectives progress through remem-

bering, understanding, applying, analyzing, and

culminating in the creation of knowledge. Each ob-

jective is meticulously crafted to empower students in

their exploration of generative AI tools, emphasizing

not only the acquisition of knowledge but also the

practical application and critical evaluation of these

skills.

The students can . . .

1. Remembering

. . . recall and list Generative AI tools, including

text-to-text tools like ChatGPT and Poe, as well

as text-to-image tools such as Ideogram and Bing

Image Creator.

. . . can remember how GenAI tools might inﬂu-

ence cultural sensitivity in content generation.

2. Understanding

. . . explain the process of prompt engineering.

. . . understand how speciﬁc prompts inﬂuence the

output of generative AI tools.

. . . describe the basic principles underlying the

functionality of both text-to-text and text-to-

image Generative AI models.

AI Wanderlust: A Roadmap to Integrating GenAI Tools in the Classroom Fostering Critical AI Literacy

525

3. Applying

. . . formulate well-crafted prompts for text-to-text

and text-to-image Generative AI tools, showcas-

ing their understanding of these tools’ functional-

ities.

. . . utilize content that enriches the virtual travel

experience in alignment with project objectives.

4. Analyzing

. . . examine how GenAI handles cultural aspects,

analyzing its impact on cultural sensitivity in con-

tent generation.

. . . compare and contrast the strengths and limita-

tions of text-to-text and text-to-image Generative

AI tools within the context of the virtual travel

project.

5. Evaluating

. . . assess the ethical implications of AI-generated

content, identifying potential biases, stereotypes,

or sensitive portrayals.

. . . evaluate the effectiveness and appropriateness

of solutions generated by GenAI in response to

speciﬁc prompts.

. . . assess the potential ethical concerns related to

manipulating user opinions or reinforcing existing

biases through the use of GenAI in virtual experi-

ences.

6. Creating

. . . design a comprehensive and engaging virtual

travel experience, integrating AI-generated con-

tent with visual enhancements.

. . . generate and present thoughtful reﬂections

on the impact of AI on cultural representation,

considering various perspectives and proposing

strategies for mitigating biases in AI-generated

content.

3.3 Strategies to Foster Critical

Thinking

Various strategies in task design have been identiﬁed

to nurture critical thinking skills among students. In

this context, Navarro et al. (2021) emphasize the ef-

fectiveness of Design Thinking activities. Bloom and

Doss (2019) and Swart (2017) draw attention to the

pivotal role of modern technologies in designing tasks

aimed at fostering critical thinking. Additionally, El-

der and Paul (2008) recommend the implementation

of instructional strategies that actively involve stu-

dents in thoughtful engagement with the fundamental

concepts and principles of the subject. These insights

collectively highlight diverse approaches to task de-

sign that contribute to the development of critical

thinking abilities, showcasing the potential impact of

Design Thinking activities, the integration of modern

technologies, and the incorporation of instructional

strategies for active student involvement.

Chan and Hu (2023) emphasize the critical impor-

tance of considering students’ perceptions of GenAI,

with a speciﬁc focus on concerns related to accu-

racy, privacy, and ethical issues. Understanding these

concerns becomes a foundational element in the de-

sign of GenAI learning tasks aimed at addressing and

alleviating potential apprehensions. Aligning with

this perspective, King (1992) advocates for interac-

tive and student-centered learning strategies, assert-

ing their value in promoting critical reﬂection – an

approach that can be effectively integrated into GenAI

learning tasks. Furthermore, Fujii’s 2015 emphasis on

task design as a means to address broader educational

values is particularly relevant. This work suggests

that GenAI learning tasks should be intricately de-

signed, taking into account anticipated student think-

ing and solutions, with evaluation facilitated through

post-lesson discussions. By incorporating these de-

sign strategies, the chapter provides a comprehen-

sive framework for educators aiming to create GenAI

learning experiences that not only enhance technical

skills but also address ethical considerations and fos-

ter critical reﬂection.

To foster critical reﬂection on the ethical and social

implications of using GenAI in the virtual travel ex-

perience project, the following design strategies can

be incorporated:

1. Ethical Dilemma Scenarios. Introduce ethical

dilemma scenarios related to AI-generated con-

tent in the travel experience. Ask students to con-

sider situations where AI may unintentionally per-

petuate stereotypes or biases. Encourage them

to discuss and address these ethical challenges in

their project.

2. Guided Ethical Frameworks. Provide students

with ethical frameworks or guidelines relevant to

AI applications. For example, discuss principles

like fairness, transparency, and accountability in

AI. Encourage students to incorporate these prin-

ciples in their design and critically reﬂect on how

well they align with ethical considerations.

3. Expert Perspectives and Guest Speakers. Invite

guest speakers or experts in AI ethics and social

implications to provide insights and perspectives.

This can offer students a deeper understanding of

the ethical challenges associated with AI. Allow

time for Q&A sessions to encourage student en-

gagement. Alternatively, YouTube videos can be

watched and discussed in the classroom.

CSEDU 2025 - 17th International Conference on Computer Supported Education

526

4. Debates and Discussions. Organize class debates

or discussions on ethical and social aspects of AI.

Assign roles to students, such as advocates for AI

in education and those who raise ethical concerns.

This encourages students to critically analyze dif-

ferent perspectives and strengthens their ability to

form well-grounded opinions.

5. Scenario Planning. Incorporate a scenario plan-

ning exercise where students anticipate poten-

tial social consequences of their virtual travel ex-

perience. Ask them to consider how different

user groups may interpret or be affected by AI-

generated content. Encourage proactive thinking

about mitigating negative impacts.

6. User Feedback and Iterative Design. Empha-

size the importance of user feedback in the design

process. Encourage students to gather feedback

on the ethical and social aspects of their virtual

travel experience from peers, teachers, and poten-

tial users. Use this feedback as a basis for iterative

design and improvement.

7. Real-world Case Studies. Introduce real-world

case studies where AI technologies have raised

ethical concerns. Discuss how companies or or-

ganizations have addressed these challenges or the

consequences of failing to do so. Relate these case

studies to the students’ projects, prompting them

to consider potential pitfalls and solutions.

8. Reﬂective Journals. Integrate reﬂective journal-

ing into the project, where students document

their thoughts, challenges, and decision-making

processes related to ethical considerations. This

ongoing reﬂection helps students develop a deeper

understanding of their own evolving perspectives.

9. Multidisciplinary Collaboration. Encourage

collaboration with students from other disciplines,

such as ethics, sociology, or philosophy. This in-

terdisciplinary approach promotes a broader un-

derstanding of ethical and social implications and

encourages diverse perspectives.

10. Public Awareness Campaigns. Task students

with creating public awareness campaigns within

their virtual travel experiences. This could involve

informing users about the ethical considerations

of AI, promoting responsible use, and encourag-

ing users to critically reﬂect on the content.

By incorporating these design strategies, you cre-

ate an environment that not only teaches students

about GenAI but also encourages them to critically

reﬂect on the ethical and social dimensions of their

projects.

4 CONNECTING THE AI

LITERACY MODEL AND THE

AI WANDERLUST PROJECT

The AI Literacy Model, adapted from Baacke’s Me-

dia Competence Model, provides a robust theoreti-

cal framework that aligns seamlessly with the prac-

tical implementation of the ”AI Wanderlust” project.

This connection ensures that the project not only in-

troduces students to Generative AI (GenAI) tools but

also fosters the critical, practical, and ethical skills

needed to navigate AI responsibly. Below is an ex-

ploration of how the dimensions of the AI Literacy

Model are applied within the context of the project.

The AI Literacy Model encompasses four key di-

mensions: AI Criticism, AI Knowledge, AI Usage,

and AI Design. AI Criticism encourages students to

analyze AI-generated content for biases, inaccuracies,

and ethical concerns, addressing issues like stereo-

types and cultural insensitivity. AI Knowledge fo-

cuses on understanding AI tools and systems, includ-

ing machine learning and prompt engineering, with

hands-on experience using tools like ChatGPT and

text-to-image generators. AI Usage involves applying

AI to solve real-world problems, where students cre-

ate content and iterate through design and feedback.

Finally, AI Design emphasizes ethical AI use, encour-

aging students to reﬂect on fairness, transparency, and

cultural sensitivity in their projects, fostering a deeper

understanding of responsible AI design.

By incorporating these dimensions into the ”AI

Wanderlust” project, students are not only introduced

to the technical aspects of GenAI tools but also en-

couraged to critically reﬂect on their applications and

ethical implications. This approach ensures that stu-

dents develop a well-rounded understanding that goes

beyond technical proﬁciency, equipping them with

the necessary skills to engage thoughtfully with AI

in society.

Through this theoretical framework, the project

offers both a practical and reﬂective educational ex-

perience, embodying the principles of the AI Literacy

Model and ensuring that students are prepared to nav-

igate the complexities of AI responsibly.

5 CONCLUSION

In summary, integrating Generative AI (GenAI) into

high school education is a crucial step in preparing

students for the demands of an increasingly AI-driven

world. As GenAI tools become an integral part of stu-

dents’ daily lives (Forman et al., 2023; Chan and Lee,

AI Wanderlust: A Roadmap to Integrating GenAI Tools in the Classroom Fostering Critical AI Literacy

527

2023), the educational system must respond by incor-

porating these tools into learning environments — not

as optional enhancements but as essential components

of digital literacy and critical AI education.

The proposed concept, ”AI Wanderlust,” serves as

a practical illustration of how GenAI can be meaning-

fully embedded into classroom practice. By engag-

ing students in the creation of a virtual travel experi-

ence, the project invites them to explore the creative

potential of GenAI while reﬂecting on its broader im-

plications. This approach aligns with a structured

roadmap for fostering critical AI literacy, emphasiz-

ing the development of key competencies such as cre-

ativity, problem-solving, and ethical reasoning.

However, such initiatives must go beyond techni-

cal skill development. Teacher guidance plays a piv-

otal role in helping students critically engage with the

ethical dimensions of GenAI, from identifying biases

to reﬂecting on the societal impact of AI-generated

content. The combination of hands-on experimenta-

tion and guided reﬂection ensures that students are not

merely passive users of AI tools but active, thought-

ful participants in shaping how these technologies are

used and understood.

Positioned within the broader discourse on AI lit-

eracy, this conceptual framework highlights the need

for proactive, ethically informed educational prac-

tices. It underscores the importance of equipping the

next generation with both the skills and critical aware-

ness required to navigate the complex, evolving land-

scape of AI responsibly and thoughtfully.

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