Rafael Alves ITM

From academics to Aidemics - Unpacking the human–AI symbiosis in higher education

Leitura de 32 min

From Academics to Aidemics: Unpacking the human–AI Symbiosis in Higher Education

🎯 Resumo

Abstract Original

The integration of artificial intelligence (AI) into higher education is reshaping both academic roles and organizational practices. This transformation is not merely technical; it also introduces psychological and ethical tensions as academics negotiate new forms of work. This qualitative study advances the concept of Aidemics—academics who efficiently, ethically, and critically employ AI to enhance their professional practice—and examines how AI reconfigures intellectual tasks while challenging traditional academic identities. Drawing on semi-structured interviews with twenty academics (informed by five pilot interviews), we show that Aidemics engage in a symbiotic, human-in-the-loop relationship with AI: they exploit efficiencies for routine tasks, yet safeguard creative and critical work, actively auditing AI outputs and setting clear boundaries of use. Participants also surface structural concerns—framed as AI colonialism—about the potential of AI to entrench power asymmetries and epistemic inequities in global knowledge production. Key challenges include hallucinations, erosion of human agency, and unequal access to AI tools. We argue for policies and professional-development strategies that preserve human agency and ethical judgment while enabling responsible, explainable, and context-sensitive AI use. The findings specify the competencies of Aidemics and outline supports for balanced human–AI collaboration at individual and institutional levels.

📚 Bibliografia

YURDUNKULU, Adem; BULUT, Mehmet Akın; GÖÇEN, Ahmet. From academics to Aidemics: Unpacking the human–AI symbiosis in higher education. Acta Psychologica, v. 261, p. 105796, nov. 2025.

🧠 Minhas Notas & Análise

🎯 Objetivo

  • O estudo busca explorar como os acadêmicos utilizam as tecnologias de Inteligência Artificial (IA) e de que forma essas ferramentas transformam os seus papéis acadêmicos tradicionais.
  • Especificamente, a pesquisa visa descobrir as características acadêmicas, sociais, tecnológicas, psicológicas e institucionais que definem o fenômeno dos “Aidemics” — um termo cunhado para descrever acadêmicos que empregam amplamente ferramentas de IA em suas rotinas com eficiência, consideração ética e consciência crítica.

🧬 Método

  • A pesquisa adotou uma metodologia qualitativa com design fenomenológico para capturar as experiências e percepções vividas pelos indivíduos.
  • Foram realizadas entrevistas semiestruturadas de 25 a 60 minutos (presenciais ou por videoconferência) com 20 acadêmicos atuantes na Turquia, provenientes de diversas disciplinas e instituições.
  • As perguntas foram validadas através de 5 entrevistas piloto prévias.
  • Os participantes foram divididos propositalmente em dois grupos: adotantes iniciais com vasta experiência e publicações em IA () e usuários moderados sem publicações na área, mas com uso prático no dia a dia ().
  • Os dados transcritos foram avaliados utilizando análise temática seguindo os seis passos de Braun e Clarke (2006).

🏆 Resultados

  • Definição do Perfil “Aidemic”: O estudo identificou que os “Aidemics” possuem características chave: utilizam a IA para múltiplas tarefas, possuem alto letramento em IA, demonstram habilidades eficientes de prompting, usam a IA como assistente pessoal, protegem seu pensamento crítico e criativo, navegam por questões éticas e vieses, adaptam seus métodos de ensino, mantêm o julgamento humano e defendem o acesso igualitário à tecnologia.
  • IA como Ferramenta Multifacetada: A IA é vista principalmente como um parceiro de brainstorming e um assistente multitarefa que agiliza edições, revisões, triagem de literatura e resumos. Além disso, atua como uma força de inclusão ao reduzir barreiras linguísticas para pesquisadores não nativos em inglês.
  • Desafios e Preocupações Éticas: Os participantes relataram fortes preocupações com a integridade acadêmica (riscos de plágio e perda de originalidade), segurança e privacidade de dados, e a “divisão digital” gerada pelos custos de versões premium das ferramentas. Também emergiu o conceito de “colonialismo de IA”, alertando para o risco de as ferramentas reproduzirem padrões e discursos predominantemente ocidentais.
  • Adaptação e Futuro da Educação: A integração da IA exige uma reestruturação das avaliações tradicionais, priorizando tarefas que a IA não consegue realizar facilmente. O estudo prevê uma mudança no papel do professor (de transmissor de conhecimento para mentor e guia emocional) e o crescimento de microcursos para preencher lacunas de letramento digital.
  • Simbio-Colaboração (Humano-IA): A IA atua como uma “extensão do cérebro” para aumentar a capacidade cognitiva, mas exige supervisão constante. O ceticismo pragmático é necessário para lidar com as “alucinações” (informações falsas geradas pela IA) e com a falta de profundidade acadêmica das ferramentas. A transparência no uso dessas tecnologias é considerada fundamental.

📝 Comentários Extras

  • O artigo oferece uma visão equilibrada que foge do pânico do “fim da academia” e da empolgação cega da “automação total”.
  • A formulação do conceito de Aidemic é uma excelente ferramenta teórica para discutir a identidade docente nesta nova década. Ele reforça que o domínio de prompts e o letramento em IA não são apenas “habilidades de TI”, mas competências epistemológicas e pedagógicas fundamentais.
  • A menção ao “Colonialismo de IA” e o viés eurocêntrico/norte-americano dos LLMs é um ponto crítico raramente discutido com a profundidade necessária na rotina acadêmica diária, merecendo destaque em futuras pesquisas.

🔗 Conexões do Cofre

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🔣 Bibliografia Latex

@article{yurdunkulu2025,
  title = {From Academics to {{Aidemics}}: {{Unpacking}} the Human--{{AI}} Symbiosis in Higher Education},
  shorttitle = {From Academics to {{Aidemics}}},
  author = {Yurdunkulu, Adem and Bulut, Mehmet Ak{\i}n and G{\"o}{\c c}en, Ahmet},
  year = 2025,
  month = nov,
  journal = {Acta Psychologica},
  volume = {261},
  pages = {105796},
  issn = {00016918},
  doi = {10.1016/j.actpsy.2025.105796},
  urldate = {2026-06-11},
  langid = {english},
}

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🖍️ Notas e Destaques

  • This qualitative study advances the concept of Aidemics—academics who efficiently, ethically, and critically employ AI to enhance their professional practice—and examines how AI reconfigures intellectual tasks while challenging traditional academic identities. (p. 1)
  • On one hand, some scholars express AI-induced anxiety about the future of academic careers, alongside concerns over potential misuse and negative impacts on student learning (Verano-Tacoronte et al., 2025). On the other hand, proponents highlight substantial benefits, including real-time support, enhanced creativity, improved efficiency, and personalized learning experiences (Hamzah et al., 2025; Nguyen, 2025; Varghese, 2024). (p. 1) Comentário: Há sempre dois lados exremos. Me parece que no geral o meio do caminho é sempre a solução
  • She asserts that “Fear of AI should not be the reason we reject AI, and fear of being left behind by AI’s rapid evolution should not be the reason we accept it hastily”. (p. 1)
  • Additionally, eleven distinct roles were frequently cited in academic role classifications: technologist, manager, co-learner, designer, knowledge expert, researcher, facilitator, assessor, adviser-counselor, e-tutor, and mentor. (p. 2) Comentário: papreis principais de um professor acadêmico
  • To capture this shift, we introduce the term Aidemic, describing academics who use AI efficiently, ethically, and critically to augment traditional service, teaching, and research roles. (p. 2) Comentário: Definição básica de um Aidemics
  • Fig. 1. Academic tasks of academics (taken from Wardell, 2021). (p. 2) Comentário: Figura fundamental para entender as atividades que eum professor/pesquisador tem que realiza.
  • Grounded frameworks of human learning such as Cognitive Load Theory (Sweller, 1988), Bloom’s Taxonomy (Bloom, 1956), and SelfDetermination Theory (Deci & Ryan, 1985) (p. 3) Comentário: Teorias do aprendizado humano.
  • highlighting that introducing AI into academia should not just prioritize efficiency, but also preserve autonomy, meaningful engagement, and participatory work design. (p. 3)
  • However, this evolution raises significant concerns about scholarly authenticity, academic deskilling, and erosion of intellectual labor (Octaberlina et al., 2024; Perkins & Roe, 2024; Watermeyer et al., 2024). (p. 3) Comentário: A evolução tecnológica sempre traz pontos importantes de atenção.
  • Building on this foundation, the present study aims to explore how academics use AI technologies and how these tools transform their academic roles. Specifically, the research seeks to uncover the academic, social, technological, psychological, and institutional features that together constitute “Aidemics”—a term defined here as academics in higher education who extensively employ AI tools in their professional endeavors with efficiency, ethical consideration, and critical awareness. (p. 3) Comentário: Objetivo do trabalho
  • RQ1. In what ways do AI technologies transform the traditional roles of academics? RQ2. What are the academic, social, technological, psychological, and institutional features that define the phenomenon of “Aidemics”? (p. 3) Comentário: Perguntas de pesquisa fundamentais
  • This study employs a qualitative research methodology, specifically using semi-structured interviews to gain in-depth insights into how academics experience and integrate AI into their work. (p. 3) Comentário: Entreistas semiestruturadas com perguntas definidas.
  • but only responses from the subsequent 20 main participants were included in the findings. (p. 4) Comentário: 20 participantes analisados, após 5 de testes. Os de teste não foram incluídos.
  • he dominant themes—including time efficiency, ethics, originality, research enhancement, personalized teaching, and concerns about overreliance—emerged across both groups. (p. 4) Comentário: Pontos principais levantados pelos entrevistados.
  • Frequently used AI and digital tools by all participants in Table 1 included ChatGPT, Gemini, Typeset, Gamma, Perplexity, Claude AI, Bard, Scite, Quizlet, Grammarly, Deeply, Canva AI, among others (p. 4) Comentário: Conjunto de ferramentas
  • All interviews were transcribed verbatim and analyzed using thematic analysis (Braun & Clarke, 2006). (p. 4) Comentário: Utilização de análise temática para avaliação dos dados.
  • Following Braun & Clarke’s (2006) six steps, we (1) familiarized (p. 4) Comentário: Pontos de avaliação
  • ourselves with the data, (2) generated initial codes, (3) searched for candidate themes, (4) reviewed themes against coded extracts and the full data set, (5) defined and named themes, and (6) produced the report (p. 5) Comentário: Pontos de avalização
  • The unit of analysis was the participant. When a participant mentioned a concept multiple times across different interview questions or contexts, it was counted only once. (p. 5) Comentário: Cada participante conta como uma medida na avaliação em questão.
  • Interview questions & some sample responses. (p. 5) Comentário: Apresenta as questões e a possibilidade de resposta.
  • we propose to use the term “Aidemics” as seen in Fig. 2 to describe academics who display symbiotic relationships with AI tools and are proficient in leveraging AI technologies to enhance their teaching, research, administrative roles, and more (p. 7) Comentário: Proposta do termo AIdemicos
  • that develop their students’ 4Cs (critical thinking, creativity, communication, and collaboration) (p. 9) Comentário: Desenvolvimento das habilidades básicas dos alunos.
  • what we term “Aidemics.” Fostering this identity requires promoting the responsible, explainable, and reasonable use of AI while preserving human agency, ensuring that AI integration enhances academic work without compromising critical thinking or ethical standards. (p. 10) Comentário: Característica principal doa AIdemicos
  • The most widely reported benefits cluster around productivity: participants consistently described AI as a multi-tasking assistant and a research/teaching fellow that streamlines editing and proofreading, supports grammar and style, triages literature, and produces quick summaries—freeing time for higherorder tasks and reducing reliance on external editing services. A second, strongly voiced benefit concerns creativity. (p. 10) Comentário: Produtividade parece ser sempre o ponto principal da utilização das ferramentas de GenAI.
  • Romeo and Alpha mentioned the creation of Sakana AI, a tool designed to streamline academic research, suggesting that in the near future, researchers might primarily focus on decision-making and strategic direction in their fields, while AI handles the more time-consuming aspects of writing, data gathering and analysis. (p. 10) Comentário: Será que essa mudança de rota para pesquisa acontecerá mesmo?
  • Despite challenges related to access and infrastructure, participants recognized AI’s potential to be an inclusive and equalizing force in academia. (p. 11) Comentário: A barreira lingúistica é cada vez menor, mas a desigualdade de acesso pode ser um problema.
  • streamline academic work, yet it also introduces new vulnerabilities—from plagiarism risks to unequal access tied to premium features, and from insecure data handling to the reproduction of dominant, Western-centric discourses. (p. 11) Comentário: Problema central dos modelos.
  • Taken together, the pattern suggests that responsible AI in higher education requires coupling innovation with integrity-aware assessment design, privacy-by-design infrastructure, equitable access and training, bias auditing and critical data practices, and formal institutional policies. Without these guardrails, efficiency gains risk entrenching new forms of unfairness and epistemic imbalance. (p. 11) Comentário: Definição do uso de IA de forma responsável.
  • This suggests a growing trend towards micro-courses and targeted training programs designed to quickly bring educators up to speed with AI technologies (Bulut et al., 2025). (p. 13) Comentário: Microcursos e micro certificações parece ser uma crescente, especialmente para formação contnuada ofertada pelas próprias instituições de ensino.
  • This statement reflects a broader recognition that AI literacy is essential not only for technology-related fields but for all areas of study. By demonstrating the practical use of AI tools in the classroom, academics can guide students in developing their own competencies with these technologies (Karanfilog ̆lu & Bulut, 2025). (p. 13) Comentário: Literacia de IA é crucial
  • Finally, participants argued for a value-driven vision: as automation expands, universities should double down on the uniquely human—ethical reasoning, meaning-making, and the ability to shape new social and scientific paradigms. (p. 13) Comentário: Caracteristicas inerentemente humanas serão cada vez mais importantes no contato professor e aluno.
  • Sun et al. (2024) explains in their study by categorizing distorted information in AI-generated content using ChatGPT as a case study, identifying 8 main error types and 31 subtypes, thereby offering a comprehensive framework to guide risk assessment, user awareness, and the improvement of AI tools. (p. 15) Comentário: Lista de erros que podem acontecer em sistemas de IA.

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