A Young Researcher’s Dual Lens: A Twofold Autoethnographic

Exploration of Generative AI in the Realms of Doing Research and

Teaching Computer Science and Media Design Education

Lisa Kuka

, Corinna H

ormann

and Barbara Sabitzer

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

Keywords:

Autoethnography, Artiﬁcial Intelligence, AI in Education, Teaching, Experience, GenAI, PhD Student.

Abstract:

This research paper unfolds the narrative journey of a young researcher immersed in the world of generative

artiﬁcial intelligence (GenAI) tools. This autoethnographic study explores a PhD candidate’s experiences in

STE(A)M education by balancing teaching and research roles while integrating GenAI tools to streamline

workﬂows, create teaching materials, and support educational processes. Findings reveal the transformative

potential of AI in addressing challenges faced by educators and researchers, from time management to lan-

guage barriers, while also emphasizing the importance of ethical considerations and ongoing professional

development. Set in an Austrian university and vocational high school, the study examines AI’s transforma-

tive impact on teaching and research. Methodologically, the study adopts an autoethnographic framework,

providing an immersive exploration of the challenges, beneﬁts, and evolving experiences encountered while

integrating GenAI-powered tools in academic endeavors. The ﬁndings underscore the transformative impact

of GenAI on literature research, methodological planning, and the drafting process, shedding light on the po-

tential of GenAI to support young researchers in STE(A)M ﬁelds. However, the study also reveals challenges,

such as the risk of hallucination by AI tools and deskilling, prompting a call for a balanced integration of AI

tools. The narrative concludes by discussing the implications for young researchers in the STE(A)M domain

and the broader educational landscape. Emphasis is placed on the importance of continuous improvement and

teacher training in the ever-evolving digital education landscape.

1 INTRODUCTION

As a young researcher pursuing a PhD, you face sig-

niﬁcant challenges, from mastering diverse research

methodologies to navigating thousands of academic

papers and receiving critical feedback from peers.

The advent of generative AI tools, such as ChatGPT,

offers a potential solution to these obstacles, trans-

forming how researchers and educators approach their

work (Cooper, 2023).

The rapidly evolving landscape of STE(A)M ed-

ucation has seen increasing adoption of AI-powered

tools, signiﬁcantly impacting both research and teach-

ing. These tools present opportunities to streamline

workﬂows and enhance learning, but they also in-

troduce challenges, such as reliance on AI and eth-

ical concerns (Cooper, 2023). While prior research

https://orcid.org/0000-0002-0000-5915

https://orcid.org/0000-0002-4770-6217

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

highlights the beneﬁts and drawbacks of AI in edu-

cation, studies on the personal experiences of PhD re-

searchers and teachers remain limited. To address this

gap, this study explores the research question: What

are the opportunities and challenges associated with

the early adoption of Generative AI in vocational and

higher education settings?

At the College for Higher Vocational Education

(levels 9-13) where I work, the implementation of AI

has emerged as a key innovation, addressing the dual

demands of vocational training and pedagogical ex-

cellence. AI tools streamline lesson planning, student

engagement, and administrative tasks, alleviating the

heavy workload of teaching and fostering success for

both students and educators. While a supplementary

paper presented at an international conference in 2024

addressed this topic, this paper focuses on summa-

rizing key insights and exploring previously underex-

plored aspects such as teacher development.

As a research assistant and doctoral candidate, I

navigate the intersection of AI in education, where

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

A Young Researcher’s Dual Lens: A Twofold Autoethnographic Exploration of Generative AI in the Realms of Doing Research and Teaching Computer Science and Media Design Education.

DOI: 10.5220/0013226200003932

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 299-306

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

299

theoretical frameworks meet practical application.

My dual roles as teacher and researcher offer a unique

perspective, bridging scholarly discourse with the

lived experiences of educators. This autoethnogra-

phy reﬂects on the synergy between these roles, ex-

ploring how classroom experiences inform research

and vice versa, while highlighting the connections be-

tween theory, practice, and innovation.

This paper provides an autoethnographic ac-

count of a researcher’s experiences with AI tools in

STE(A)M education, focusing on their integration

into teaching and research. Section 2 reviews related

work PhD challenges, AI terminology, and vocational

education. Section 3 outlines the autoethnographic

methodology and its application in this study. Sec-

tion 4 highlights generative AI’s transformative po-

tential and limitations through a narrative approach

that combines personal experiences with analytical

insights. Finally, the conclusion discusses implica-

tions for educators and researchers, offering recom-

mendations for ethical and effective integration of AI

in education.

2 RELATED WORK

Undertaking a PhD, particularly in a foreign con-

text, presents a myriad of academic and non-academic

challenges (Elliot et al., 2016). For PhD students

concurrently fulﬁlling teaching responsibilities, the

dearth of resources and support underscores the im-

perative for enhanced communication and collabora-

tion between learning developers and doctoral candi-

dates (Kantcheva and Sum, 2023). The dual role of

a PhD student and teacher engenders a unique blend

of challenges and opportunities throughout the doc-

toral journey (Wolstenholme, 2008). The discourse

surrounding the value of teaching experience during

postgraduate study remains contentious, with diver-

gent perspectives positing it as either a valuable asset

or an encumbrance (Homer, 2018).

Within the expansive realm of technological evo-

lution, the terms AI (Artiﬁcial Intelligence), ML (Ma-

chine Learning), and DL (Deep Learning) delineate

interconnected yet distinct facets of computational

intelligence. AI embodies the overarching concept

of endowing machines with the ability to simulate

human intelligence, engaging in tasks ranging from

problem-solving to natural language understanding.

ML, as a subset of AI, concentrates on systems that

learn and improve from experience without explicit

programming, adapting their performance based on

data input. DL further reﬁnes the scope, represent-

ing a specialized form of ML that involves neural net-

works with multiple layers, enabling the processing

of intricate data for more sophisticated tasks (Lalitha,

2021).

Inside this complex technological landscape, Gen-

erative AI emerges as a pivotal player. Positioned

within the broader AI landscape, Generative AI refers

to systems capable of producing new content au-

tonomously, transcending mere data analysis to fos-

ter creativity and innovation. Speciﬁcally, Large Lan-

guage Models (LLMs) represent a notable subset of

Generative AI, excelling in comprehending and gen-

erating human-like language. Their capacity to under-

stand context, nuances, and linguistic structures em-

powers them to contribute to a spectrum of applica-

tions, from content creation to conversational inter-

faces.

Additionally, within the scope of Generative

AI, Generative Adversarial Networks (GANs) hold

prominence. GANs operate on a unique adversar-

ial training paradigm, involving two neural networks

– a generator and a discriminator – working in tan-

dem (Goodfellow et al., 2014). This dual-network

structure enables GANs to generate realistic content,

bridging the gap between data-driven algorithms and

the creative potential of AI.

The rise of Generative AI has prompted a criti-

cal reevaluation of lifelong learning and education.

It also raises concerns regarding the current educa-

tional paradigm, which is often centered around dis-

posable knowledge (Class and De la Higuera, 2024).

The integration of AI tools, such as ChatGPT, into

lifelong learning offers both opportunities and chal-

lenges, highlighting the need for a balanced approach

(Tomaszewska, 2023). As education transitions to-

wards Education 5.0, Generative AI is expected to

transform the learning landscape by democratizing

access to knowledge and enhancing human capabil-

ities (Gowda, 2023). Nevertheless, the responsible

development and implementation of AI are essential

to fully harness its beneﬁts while mitigating potential

risks. Collectively, the literature emphasizes the sig-

niﬁcance of continuous self-development, ethical AI

usage, and a shift towards deeper, more meaningful

learning in the context of Generative AI.

3 METHODOLOGY

3.1 Autoethnography as Method

Adopting an autoethnographic framework (Cohen

et al., 2018), this study merges personal experiences

and scholarly analysis, allowing for a holistic explo-

ration of the impact of generative AI on research and

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300

educational practices of a young academic. The au-

toethnographic approach involves an introspective ex-

amination of the researcher’s interactions and engage-

ments with the AI tools, blending subjective experi-

ences, reﬂections, and interpretive analysis. The data

collection process encompasses interaction logs, per-

sonal reﬂections, and reﬂexive narratives, capturing

the researcher’s encounters with GenAI tools such

as ChatGPT across various research stages. This

methodological choice aims to provide an in-depth,

situated understanding of the inﬂuence of AI on the

researcher’s workﬂow, enabling the contextualization

of personal encounters with generative AI within

broader educational and research landscapes. Ethi-

cal considerations regarding the use of AI, as well

as the subjectivity and biases inherent in the au-

toethnographic approach, will be critically examined

throughout this study.

3.2 Process of Data Collection

The data collecting process is depicted in Figure 1.

The duration of the study was three months in which

all observations and reﬂections were noted in the form

of a journal. Observations were frequently recorded

in concise keywords, capturing immediate insights.

However, at the conclusion of each week and again

following each month, a more comprehensive reﬂec-

tion was undertaken to delve deeper into the obser-

vations and experiences. The ideas and thought pro-

cesses underwent regular review at the end of each

week and month, aiming to assess their alignment

with existing research on the topic and their resonance

with the researcher’s own experiences. Furthermore,

screenshots were taken and added to the journal.

Figure 1: Data Collection Process of One Month.

3.3 Data Analysis and the Formation of

the Narrative

These items constituted the primary dataset for the

study, facilitating an in-depth examination of the re-

searcher’s experiences through both narrative and vi-

sual analyses. Initially, the analytical process in-

volved identifying common tasks and challenges en-

countered by young researchers. Subsequently, fo-

cus shifted to delineating areas where GenAI tools

could provide support, exemplifying the evolution

of research practices from traditional to AI-infused

workﬂows. Employing this methodological approach,

the study aimed to offer a nuanced understanding of

how GenAI tools are integrated and utilized within

academic contexts. The ﬁndings are presented in

line with the convention of autoethnographic stud-

ies (Cluxton-Corley, 2017; Cohen et al., 2018) as a

narrative, in which literature is seamlessly integrated

throughout the narrative. Moreover, various visual

aids, including workﬂow graphics, were generated to

visually depict speciﬁc research aspects.

4 THE YOUNG RESEARCHER’S

LENS

4.1 Obstacles Young Researchers Face

The outcome of the reﬂection process at the end of the

initial month of the study was a mind map of obsta-

cles and challenges young researchers face. The ﬁnd-

ings of this introspection can be substantiated by rele-

vant literature. Although there are hardly any studies

that deal with the difﬁculties young researchers in Eu-

rope are facing, especially from a young researcher’s

perspective, global studies support this subjective im-

pression and list difﬁculties such as lack of mentoring,

funds, heavy workload (Kumwenda et al., 2017), aca-

demic writing skills (Chatzea et al., 2022; Kumwenda

et al., 2017), insufﬁcient research skills and uncertain-

ties (Kumwenda et al., 2017; Alarc

ao, 2017), stress

and time management (Bocar, 2009), and job insta-

bility (Ameen et al., 2019). The ﬁndings of the study

were assembled into a metaphorical house as can be

seen in Figure 2.

The foundation for stability and growth in a PhD

journey relies on robust mentorship, a strong super-

visor relationship, effective time management, and

proﬁciency in both EFL and academic English. Key

pillars support this structure: the research process

(methodologies, data analysis, and deﬁning research

goals), publishing research (writing, citations, and

tools like LaTeX and Mendeley), and lecture prepara-

tion (planning, organization, and digital skills). Pre-

sentation skills unify these elements, crucial for shar-

ing ﬁndings, securing funding, and teaching. To-

gether, these components form a metaphorical house,

with the PhD as its pinnacle, representing the chal-

lenges faced by young researchers in achieving doc-

toral success.

A Young Researcher’s Dual Lens: A Twofold Autoethnographic Exploration of Generative AI in the Realms of Doing Research and

Teaching Computer Science and Media Design Education

301

Figure 2: Challenges of a PhD.

Based on this analysis the researcher proceeded

to identify speciﬁc stages within the research process

where the integration of GenAI could offer assistance

and streamline workﬂows which is discussed in the

next chapters.

4.2 Impact on Research Workﬂow –

from Traditional to AI-Infused

Every PhD journey begins with literature research,

traditionally relying on platforms like Web of Sci-

ence, Google Scholar, and Scopus. AI tools such as

Elicit, Perplexity, Scite, Research Rabbit, and SciS-

pace have transformed this process by streamlining

workﬂows, improving citation practices, and enhanc-

ing academic language proﬁciency, particularly for

EFL researchers. These tools accelerate literature

reviews and deepen content understanding, as evi-

denced by the contrast with previous scoping reviews

conducted without AI, highlighting their efﬁciency

gains in scholarly endeavors.

Research involves meticulous planning, including

methodology, data collection, and analysis, to for-

mulate precise questions, hypotheses, and outcomes.

During the literature review, ChatGPT was used as

both a linguistic resource and an ideation collabora-

tor, clarifying terminology and offering suggestions

for research design and potential questions. While not

blindly adopted, these insights provided a foundation

for reﬁnement, showcasing the synergy between hu-

man expertise and AI in enhancing research depth and

scope.

The shift from a traditional to AI-infused liter-

ature workﬂow can reshaped how researchers con-

duct and engage with academic research. The term

“AI-infused” reﬂects integration with AI as a com-

plementary component, whereas “AI-powered” sug-

gests a primary reliance on AI, emphasizing a nu-

anced approach to its integration in research work-

ﬂows. The traditional workﬂow relied on platforms

like Google Scholar, Scopus, Web of Science, and

similar academic databases for literature searches.

However, young researchers and students were often

left to navigate the challenges of reading and com-

prehending academic papers and writing in academic

language with little support. The AI-infused approach

incorporates additional tools early on, such as Elicit,

SciSpace, and Research Rabbit, which automate as-

pects of researchers’ workﬂows, enhancing literature

searches, comprehension, and academic vocabulary

while illustrating seamless integration of references.

While these tools streamline research, researchers,

particularly novices, must maintain academic in-

tegrity by cross-referencing AI-generated summaries

with original sources. Tools like ChatPDF and AskY-

ourPDF facilitate deeper engagement with content,

enabling comprehension and validation to ensure ac-

curate interpretations.

Beyond research, tools like Grammarly and Quill-

Bot enhance language proﬁciency by improving

grammar, vocabulary, and clarity. Their integration

with Elicit creates a comprehensive support system,

helping researchers reﬁne written content and main-

tain high linguistic standards in their scholarly work.

4.3 Supporting Young Researchers

Through GenAI

In the journey of conducting an autoethnography re-

search, the researcher integrated ChatGPT as a valu-

able tool to engage in thoughtful discussions about

the research topic. Armed with a broad conceptual-

ization of their research goals, ChatGPT proved in-

strumental in the iterative process of reﬁning their

ideas. Beyond serving as a mere conversational part-

ner, it became an active contributor to their research

endeavors. As the researcher navigated the terrain of

their research topic, the interactions with ChatGPT

led to the discovery of additional research ideas, un-

veiling unexplored dimensions of their chosen sub-

ject. Moreover, the tool played a pivotal role in the

identiﬁcation of suitable research methods, offering

insights and explanations about methods previously

unknown. ChatGPT acted as a collaborative brain-

storming partner, offering creative input for research

paper titles and enriching the scope of the autoethnog-

raphy. It also proved valuable in preparing lectures,

workshops, and lesson plans, enhancing both research

and teaching processes. The researcher used tools

like Elicit and Perplexity to navigate academic litera-

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302

ture and facilitate the literature review process. While

these tools aided in identifying relevant papers and

expediting the review, distinguishing between high-

quality and substandard contributions remained a sig-

niﬁcant challenge, especially for novice researchers.

The researcher explored generative AI’s potential to

produce nuanced and original content. While cur-

rent technology falls short of high-quality research

papers, it shows promise in drafting well-structured

paragraphs, particularly for introductions and foun-

dational discussions. AI chatbots like ChatGPT can

also act as a devil’s advocate, generating rebuttals

and novel perspectives that stimulate critical think-

ing and paper reﬁnement. These tools conserve in-

tellectual effort by enabling researchers to focus on

core research questions. For young researchers, es-

pecially non-native English speakers, ChatGPT and

tools like DeepL and QuillBot offer valuable support

with grammar, vocabulary, paraphrasing, translation,

and citations, addressing key linguistic challenges and

streamlining the writing process.

4.4 Integrating Research and Teaching

As a PhD candidate, delivering lectures indepen-

dently requires pedagogical expertise to effectively

present complex topics. Generative AI tools support

key teaching tasks, including designing adaptable les-

son plans, generating supplementary materials like

quizzes and case studies, providing constructive feed-

back based on student patterns, and streamlining or-

ganizational tasks such as reﬁning texts for commu-

nication. Additionally, GenAI aids in integrating rel-

evant research frameworks into courses, connecting

foundational topics with contemporary insights. This

synergy between research and teaching, enabled by

GenAI, enhances the educational experience by fos-

tering relevance and forward-looking innovation.

4.5 Generative AI in Qualitative

Analysis

Generative AI tools have the potential to enhance

qualitative research by facilitating thematic analy-

sis and efﬁciently identifying patterns and emergent

themes in complex datasets. These tools stream-

line workﬂows by assisting in data coding, catego-

rizing participant responses, and synthesizing liter-

ature to contextualize ﬁndings. Practical applica-

tions include summarizing themes, suggesting rele-

vant codes, drafting initial research reports, and tran-

scribing recorded interviews for analysis. Despite

these advantages, rigorous validation is necessary to

ensure reliability and mitigate ethical and method-

ological challenges. Researchers must adopt a critical

perspective, ensuring ethical use and methodological

transparency when integrating AI tools into their pro-

cesses.

4.6 Challenges and Limitations of

GenAI Tools for Researchers

The utilization of generative AI tools in research in-

troduces a spectrum of challenges and limitations.

One prominent issue involves the potential for hal-

lucination, where the generated content may deviate

from factual accuracy, posing a risk to the reliability

of research outcomes. Furthermore, there is a concern

about deskilling, as over-reliance on automated tools

might diminish researchers’ skills in critically eval-

uating and synthesizing information. Another chal-

lenge is the risk of researchers becoming complacent

and developing a dependency on AI-generated con-

tent, potentially leading to a reduced inclination to

thoroughly read and comprehend academic papers.

These challenges underscore the need for a balanced

integration of generative AI tools, emphasizing the

importance of maintaining a researcher’s core skills

and critical engagement with the scholarly literature.

The authors encountered similar challenges, exempli-

ﬁed by a notable instance of hallucination illustrated

in Figure 3, specially translated for this paper. The

ﬁgure depicts ChatGPT’s response to an inquiry re-

garding the release dates of different versions of Dig-

Comp – a European digital competency model. The

statement falsely asserts the existence of a third ver-

sion currently in development. Despite no ongoing

work on a third version, the misconception could be

rationalized, considering the frequency with which

new iterations of models and products are typically

under development.

The emergence of GenAI tools has provoked in-

tricate discussions concerning their potential ramiﬁ-

cations for academic integrity, particularly within the

domain of scholarly writing (H

ormann et al., 2024).

Despite the prolonged availability and widespread

use of AI-powered language assistance tools such

as Grammarly (2009), DeepL (2017), and QuillBot

(2017), questions have rarely been raised regarding

their legitimacy. The majority of research on schol-

arly writing tools has yielded positive results, empha-

sizing their role in providing assistance with gram-

mar, punctuation, spelling checks, synonym sugges-

tions, and paraphrasing. These tools have proven par-

ticularly beneﬁcial for non-native English speakers

in crafting scientiﬁc content, leading to notable im-

provements in machine translation accuracy. By facil-

itating the overcoming of language barriers, they con-

A Young Researcher’s Dual Lens: A Twofold Autoethnographic Exploration of Generative AI in the Realms of Doing Research and

Teaching Computer Science and Media Design Education

303

Figure 3: DigComp overview according to ChatGPT (trans-

lated from German by the author).

tribute signiﬁcantly to enhancing the efﬁciency and

quality of writing for researchers (Bhatia, 2023; Liu,

2023). Moreover, the conventional expectation that

scientiﬁc manuscripts undergo scrutiny by native En-

glish speakers or professional editing services prior

to publication has long presented a formidable ﬁnan-

cial barrier for many researchers. Consequently, the

integration of GenAI tools into the writing process

has emerged as a practical solution, democratizing

access to language reﬁnement and editorial support.

However, it is imperative to delve deeper into the

multifaceted implications of employing such tools.

While they undeniably streamline the writing process,

they also prompt a reevaluation of traditional notions

of authorship and scholarly rigor. While AI tools

can facilitate drafting outlines and composing papers,

high-quality scientiﬁc publications still require signif-

icant human input for depth and originality (H

ormann

et al., 2024). Moreover, it is essential to recognize

that the process of writing a paper represents just one

phase in the broader research endeavor, following ex-

tensive preparatory work including formulating re-

search questions, collecting and analyzing data. The

publication stage serves to communicate ﬁndings ef-

fectively to the scientiﬁc community, and GenAI tools

can greatly facilitate this process without diminishing

the researcher’s efforts. However, it is crucial to es-

tablish criteria for the ethical use of GenAI tools in

research. A methodical workﬂow is imperative, in-

volving thorough scrutiny of resources, ensuring gen-

erated text aligns with the author’s intended message,

and seeking feedback from colleagues. These steps

are more vital than ever to uphold academic integrity

amidst the increasing accessibility of AI-driven writ-

ing assistance. Moreover, the seamless integration

of AI-generated text within scholarly discourse raises

questions about the delineation between human and

machine contributions, as well as the ethical respon-

sibilities inherent in scholarly communication. Thus,

while GenAI tools offer undeniable beneﬁts in terms

of efﬁciency and accessibility, their utilization ne-

cessitates a nuanced understanding of their ethical

(such as plagiarism or bias) and epistemological im-

plications (how knowledge is produced and validated)

within the context of academic research and publica-

tion.

5 THE TEACHER’S LENS

A supplementary paper on this subject matter was pre-

sented at the CSEDU conference in 2024. Here, a

short summary is provided focusing on the key as-

pects covered in that paper. Additionally, we offer

a closer examination of topics that were not exten-

sively addressed, including the aspect of teacher de-

velopment.

5.1 Summary of Findings

The autoethnographic study yielded key insights

into the impact of generative artiﬁcial intelligence

(GenAI) on teaching and research practices in edu-

cation:

Hidden Workload. Educators devote signiﬁcant time

to tasks beyond classroom instruction, such as mate-

rial preparation and professional development. This

shared sentiment, particularly evident in language

classrooms, underscores the substantial workload ed-

ucators face.

Transformative Impact. GenAI revolutionizes class-

room dynamics, easing workloads, and reshaping

teaching and research practices in education.

Enhancing Student Assessment. While GenAI shows

promise in improving student assessment, caution is

warranted due to ethical and practical considerations,

necessitating a balanced approach in educational set-

tings.

Challenges and Concerns. Concerns arise regarding

content inaccuracies, potential deskilling of writing

abilities, and the risk of reduced teacher involvement

with overreliance on AI. Striking a balance between

leveraging AI beneﬁts and preserving the teacher’s

role is essential.

Implications for Education. Effective integration of

GenAI in education demands evidence-based guide-

lines and policies to ensure responsible usage. Educa-

tors must impart the importance of foundational skills

alongside technological tools to students.

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304

These ﬁndings underscore AI’s transformative po-

tential in education while highlighting the importance

of ethical considerations, balanced approaches, and

ongoing research to navigate its evolving integration

into educational practices Kuka and Sabitzer (2024).

5.2 Professional Teacher Development

As a PhD student holding a Pre-Doc position, de-

livering lectures is among the multifaceted respon-

sibilities. Throughout various conversations regard-

ing professional teacher development, recurring feed-

back underscores the challenge of staying abreast of

current knowledge as an educator. Despite possess-

ing academic backgrounds and familiarity with schol-

arly literature, many teachers encounter difﬁculties

comprehending complex academic content. Factors

such as the English language barrier and the pas-

sage of time since their formal education contribute

to this challenge. Following workshops hold by the

researchers demonstrating the functionalities of tools

like Elicit, SciSpace, DeepL, and ChatPDF, partici-

pants expressed a sense of relief and demonstrated

positive attitudes towards these technological aids.

The ease of use and perceived usefulness of such tools

emerged as pivotal factors inﬂuencing their adoption

(Gloria and Oluwadara, 2015; Eksail and Afari, 2019;

Chong, 2012; Kurian et al., 2019). By effectively ad-

dressing users’ needs, providing intuitive interfaces,

and little to no prior knowledge of how to use these

tools, they align with the criteria deemed essential

for integrating new technologies into practice. Their

ability to streamline tasks and enhance productivity

resonates with educators, fostering a welcoming en-

vironment for the incorporation of innovative tools

in their professional endeavors. These technological

aids may hold great potential in bridging the gap be-

tween teachers’ existing knowledge and the demands

of academic discourse, ultimately facilitating contin-

uous professional growth and development.

5.3 Teaching Material and Generation

of Tasks

In the realm of teaching, the absence of standard-

ized textbooks often prompts educators to craft their

own teaching materials, a necessity particularly pro-

nounced in rapidly evolving ﬁelds like computer sci-

ence and media design. Given the swift pace of

advancements in these disciplines, teaching materi-

als and tasks must continually adapt to remain cur-

rent while also elucidating foundational principles.

GenAI emerges as a valuable ally in this endeavor,

aiding educators in formulating lesson content, gen-

erating images, and sourcing compelling examples

and metaphors tailored to the speciﬁc context of their

class, school, or specialization. The collaborative

synergy between educators and AI tools not only sim-

pliﬁes the process of creating teaching materials but

also guarantees their pertinence and efﬁcacy in capti-

vating students with contemporary trends while rein-

forcing fundamental concepts.

6 CONCLUSION AND OUTLOOK

In conclusion, this autoethnographic study offers

a distinctive perspective on integrating generative

AI tools in both teaching and research, particularly

within vocational high school contexts. By exam-

ining practical applications – such as reﬁning liter-

ature research with ChatGPT and enhancing student

assessments – the researcher demonstrates how AI

can meaningfully enrich educational practices. The

study thoroughly addresses the ethical dimensions of

AI usage, emphasizing the need to uphold academic

standards and remain mindful of potential biases. It

further argues for a thoughtful balance between in-

novative AI solutions and the indispensable roles of

educators and researchers. As the lines between these

roles continue to blur in an increasingly digital aca-

demic landscape, the narrative underscores both the

challenges and beneﬁts of AI implementation. Ulti-

mately, this work reveals AI’s signiﬁcant potential in

personalized learning, adaptive assessment, and ad-

vanced research tools.

However, the ongoing development and integra-

tion of AI must be accompanied by sustained ethical

vigilance and professional support for educators. This

balanced approach – rooted in continuous research

and responsible adoption – will allow AI to fully sup-

port burgeoning researchers in STE(A)M ﬁelds, while

preserving the pedagogical and ethical cornerstones

essential to meaningful education.

While this study provides insights into the inte-

gration of generative AI tools in research and teach-

ing, it is limited by its autoethnographic approach,

which focuses on a single researcher’s experiences

and may not capture the broader diversity of aca-

demic contexts. The rapid evolution of AI tools also

challenges its long-term relevance. Future research

should examine diverse perspectives, long-term im-

pacts on academia, and ethical issues such as de-

pendency, bias, and integrity. Interdisciplinary stud-

ies can provide practical strategies for leveraging AI

while upholding ethical and pedagogical standards.

A Young Researcher’s Dual Lens: A Twofold Autoethnographic Exploration of Generative AI in the Realms of Doing Research and

Teaching Computer Science and Media Design Education

305

ACKNOWLEDGMENT

Various GenAI Tools were used throughout the writ-

ing process of this paper. Every paragraph was drafted

by the authors and revised with ChatGPT, Claude,

Grammarly, etc.

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