Synthetic co-authors
conceptual evaluation of biology lesson plans generated by IAGs
Abstract
This article analyzes the conceptual quality and alignment to the principles of OnLIFE Education in high school biology lesson plans generated by two multimodal language models (ChatGPT-4o and Gemini 2.5 Flash). The study begins by recognizing that hyperintelligences are already operating as synthetic co-authors in instructional planning, challenging traditional notions of authorship, curation, and schooling. A mixed-methods approach (quantitative and qualitative) was used to analyze 52 AI-generated lesson plans, which were assessed by expert biology teachers using a five-dimension rubric: conceptual accuracy, cognitive complexity, interdisciplinarity, OnLIFE alignment, and compliance with the BNCC (Brazilian National Common Core Curriculum). Results reveal an overall excellent performance (M = 3.86), with high scores in conceptual accuracy and curricular compliance, but also point to persistent gaps in interdisciplinarity and accessibility. The study concludes that, although promising, generative AIs require critical mediation to avoid reproducing disciplinocentric and technocratic logics. The article argues that active integration between teachers and LLMs, through co-design practices, should be a priority in teacher education and future empirical classroom research.
Keywords: OnLIFE education; Generative Artificial Intelligence; Synthetic co-authorship; Biology teaching; Instructional planning.
References
ALMEIDA, C. M. M. DE; FÁTIMA GREIN, L. DE; BEDIN, E. Metodologias ativas, tecnologias digitas e ferramentas metacognitivas: um curso de formação continuada. Redin-Revista Educacional Interdisciplinar, v. 13, n. 1, p. 42–62, 2024.
ALMEIDA, M. E. B. T. M. P. DE. A educação a distância na formação continuada de gestores para a incorporação de tecnologias na escola The distance education in the continuing education of principals for the incorporation of technology in school. ETD: Educação Temática Digital, v. 10, p. 186–202, 2009.
ANDERSON, L. W.; KRATHWOHL, D. R. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman, 2001.
BARRETO, C. H. DA C.; GHISLENI, T. S.; BECKER, E. L. S. Ensino Adaptativo: um Mapeamento de Perfis de Aprendizagem em Alunos da Pós-Graduação na UFN. Revista de Ensino, Educação e Ciências Humanas, 2022.
BATESON, G. Steps to an Ecology of Mind. 1st. ed. San Francisco: Chandler Publishing Company, 1972.
BESSAS, N. et al. Comparative Analysis of ChatGPT and Gemini; Implications for Junior High School Physics Education: Opportunities and Ethical Challenges. International Journal of Advanced Multidisciplinary Research and Studies, 2025.
CASTELLANI, A. M. et al. Uso de Inteligência Artificial em Sistemas de Tutores Inteligentes. Revista de Ensino, Educação e Ciências Humanas, 2024.
COSTA, A. R.; LEFFA, V. J. Produção Colaborativa de REA para o Ensino de Línguas: da Interação à Coautoria2017Disponível em: <https://doi.org/10.18264/eadf.v7i1.550>. Acesso em: 25 jun. 2025
EZIAMAKA, N. V.; ODONKOR, T. N.; AKINSULIRE, A. A. AI-Driven accessibility: Transformative software solutions for empowering individuals with disabilities. International Journal of Applied Research in Social Sciences, 2024.
FAZENDA, I. C. A. Interdisciplinaridade: história, teoria e pesquisa. 15. ed. Campinas: Papirus, 2008.
FEREBEE, S. AI and Accessibility: Breaking Barriers for People with Disabilities. Premier Journal of Artificial Intelligence, 2025.
FREIRE, P. Pedagogia da autonomia: Saberes necessários à prática educativa. São Paulo: Paz e Terra, 1996.
FUCHS, L. et al. General Educators’ Specialized Adaptation for Students with Learning Disabilities. Exceptional Children, v. 61, p. 440–459, 1995.
FUJIMOTO, M. et al. Evaluating Large Language Models in Dental Anesthesiology: A Comparative Analysis of ChatGPT-4, Claude 3 Opus, and Gemini 1.0 on the Japanese Dental Society of Anesthesiology Board Certification Exam. Cureus, v. 16, 2024.
GAGNÉ, R. M.; BRIGGS, L. J.; WAGER, W. W. Principles of Instructional Design. 4th edition ed. Fort Worth: Harcourt Brace College Publishers, 1992.
GITAHY, R.; OLIVEIRA, A. Planejamento docente na era digital: o uso de recursos educacionais abertos. Redin-Revista Educacional Interdisciplinar, v. 13, n. 1, p. 81–100, 2024.
GUIMARÃES, H. O.; BATISTA, L. K. D. S.; BATISTA, E. C. A inclusão escolar e as políticas educacionais: possibilidades e novos caminhos2017. Acesso em: 1 jul. 2025
ILLICH, I. Deschooling Society. 1st. ed. New York: Harper & Row, 1971.
KARPOUZIS, K. et al. Tailoring Education with GenAI: A New Horizon in Lesson Planning. 2024 IEEE Global Engineering Education Conference (EDUCON), p. 1–10, 2024.
LEITE, K. T. Q.; FERREIRA, M. S.; OLIVEIRA, C. S. DE. INTERDISCIPLINARIDADE E DISCIPLINARIZAÇÃO DA EDUCAÇÃO AMBIENTAL NOS CURRÍCULOS FORMAIS: UMA ANÁLISE DE PRODUÇÕES ACADÊMICAS. Itinerários de resistência: pluralidade e laicidade no Ensino de Ciências e Biologia, 2021.
LIMA TERÇARIOL, A. A. DE et al. Trilhas formativas para professores: o Google Meet como ambiente de construção teórico-metodológica. Redin-Revista Educacional Interdisciplinar, v. 13, n. 1, p. 118–139, 2024.
LOPES, M. F. Obras geradas por inteligência artificial: desafios ao conceito jurídico de autoria. CompSciRN: Artificial Intelligence (Topic), 2021.
LUO, H. et al. BPE: Exploring the Prompt Framework of Physics Exercises Generated from Bloom’s Taxonomy in LLM. 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), p. 26–29, 2024.
MERRILL, M. D. First Principles of Instruction: Identifying and Designing Effective, Efficient, and Engaging Instruction. San Francisco: Pfeiffer, 2012.
MINISTÉRIO DA EDUCAÇÃO. Base Nacional Comum Curricular. Brasília: [s.n.]. Disponível em: <https://basenacionalcomum.mec.gov.br/>. Acesso em: 28 jul. 2025.
MONAJATIPOOR, M. et al. LLMs in Biomedicine: A study on clinical Named Entity Recognition. ArXiv, v. abs/2404.07376, 2024.
PEREIRA, J. et al. INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges. Digital Government: Research and Practice, v. 6, p. 1–20, 2024.
POORNIMA, G. et al. Improving Multimodal Interactions with ChatGPT by Integrating Machine Learning with Text, Image, and Speech Processing. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), p. 780–786, 2024.
POSSA, A.; FELICE, M. DI. Desinteresse de estudantes do Ensino Médio pela escola: elementos do paradigma da Educação OnLIFE para compreensão de lacunas de desempenho do sistema de ensino brasileiro. Redin-Revista Educacional Interdisciplinar, v. 13, n. 2, p. 98–116, 2024.
POWELL, W. A.; COURCHESNE, S. Opportunities and risks involved in using ChatGPT to create first grade science lesson plans. PLOS ONE, v. 19, 2024.
REIGELUTH, C. M. Instructional-design theories and models: A new paradigm of instructional theory. [s.l.] Lawrence Erlbaum Associates, 1999. v. II
SANTOS, B. DE S. O fim do império cognitivo: a afirmação das epistemologias do Sul. [s.l.] Autêntica, 2018.
SCHLEMMER, E.; FELICE, M. DI; SERRA, I. M. R. DE S. Educação OnLIFE: a dimensão ecológica das arquiteturas digitais de aprendizagem. Educar em Revista, v. 36, 2020.
SOUSA, R. R. A. DE. Teorizando o STEAM: como integrar projetos interdisciplinares no currículo STEAM. Revista Interseção, 2024.
TEPE, M.; EMEKLI, E. Assessing the Responses of Large Language Models (ChatGPT-4, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Breast Imaging: A Study on Readability and Accuracy. Cureus, v. 16, 2024.
TORRES, N. et al. Injecting Bias through Prompts: Analyzing the Influence of Language on LLMs. 2024 43rd International Conference of the Chilean Computer Science Society (SCCC), p. 1–8, 2024.
WANG, T.; ZHOU, N.; CHEN, Z. Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation. ArXiv, v. abs/2407.05437, 2024.
ZHOU, Z. et al. Examining How the Large Language Models Impact the Conceptual Design with Human Designers: A Comparative Case Study. International Journal of Human–Computer Interaction, v. 41, n. 10, p. 5864–5880, 2025.
ZOUMPOULIDI, M.-E.; PARASKEVOPOULOS, G.; POTAMIANOS, A. BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom’s-Taxonomy-Inspired Prompts. ArXiv, v. abs/2410.04094, 2024.
