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Annexes : Bibliographie

Normanthiia – Formation des formateurs NSI & SNT

Bruno Mermet – Licence CC BY-NC-SA – 09/26

Plan

Éducation, Cognition, Pensée Critique et Apprentissage

  1. Ahmadi, N. (2023). Le développement de la créativité et de la pensée critique dans la pratique professionnelle des enseignants fonctionnaires stagiaires, lien entre auto-évaluation et pratiques de classe. L’Université Rennes 2. URL: Non disponible
  2. Akgun, M., & Toker, S. (2024). Evaluating the effect of pretesting with conversational AI on retention of needed information. arXiv preprint (abs/2412.13487). https://doi.org/10.48550/arXiv.2412.13487
  3. Anctil, D. (2023). L’atrophie cognitive induite par la technologie : analyse des effets de l’intelligence artificielle sur les capacités intellectuelles. Revue des Sciences de l’Éducation, 47(3), 165–182. URL: Non disponible
  4. Bai, L., Liu, X., & Su, J. (2023). ChatGPT: the cognitive effects on learning and memory. BrainX, 1(4), e30. https://doi.org/10.1002/brx2.30
  5. Barabel, M. (2026). L’IA générative bouleverse-t-elle les pratiques pédagogiques ?. Polytechnique Insights. https://www.polytechnique-insights.com/tribunes/digital/lia-generative-bouleverse-t-elle-les-pratiques-pedagogiques/
  6. Barshay, J. (2024). Kids Who Use ChatGPT as a Study Assistant do Worse on Tests. The Hechinger Report. http://hechingerreport.org/kids-chatgpt-worse-on-tests/
  7. Burbules, N., & Phillips, D. (2000). Postpositivism and Educational Research. Rowman & Littlefield. https://doi.org/10.5771/9781442202702
  8. Carney, S. (2022). Reimagining our futures together: A new social contract for education: by UNESCO. Comparative Education, 58(1-2). https://doi.org/10.1080/03050068.2022.2102326
  9. Chen, J. J., & Lin, J. C. (2024). Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects. Contemporary Issues in Early Childhood, 25(1), 146–153. https://doi.org/10.1177/14639491231169813
  10. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
  11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  12. Deci, E. L., & Ryan, R. M. (1985). Motivation intrinsèque et autodétermination dans le comportement humain. Plenum Press. URL: Non disponible
  13. Ennis, R. (2013). Critical Thinking Across the Curriculum. Inquiry: Critical Thinking Across the Disciplines, 28, 25-45. https://doi.org/10.5840/inquiryct20132828
  14. Essien, A., Bukoye, O. T., O’Dea, X., & Kremantzis, M. (2024). The influence of AI text generators on critical thinking skills in UK business schools. Studies in Higher Education, 49(5), 865-882. https://doi.org/10.1080/03075079.2024.2316881
  15. Facione, P. (1989). Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction. Research Findings and Recommendations, 315. URL: Non disponible
  16. Fiske, S., & Taylor, S. (2023). Cognition sociale : du cerveau à la culture (3e éd.). SAGE Publications. URL: Non disponible
  17. Flavell, J. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34, 906-911. https://doi.org/10.1037/0003-066X.34.10.906
  18. Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
  19. Giannini, S. (2023). L’IA générative et le futur de l’éducation. UNESCO, Paris. URL: Non disponible
  20. Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How does generative artificial intelligence impact student creativity?. Journal of Creativity, 34, 100072. https://doi.org/10.1016/j.yjoc.2023.100072
  21. Halpern, D., & Dunn, D. (2022). Thought and Knowledge: An Introduction to Critical Thinking. Routledge. https://doi.org/10.4324/9781003025412
  22. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Promise and Implications for Teaching and Learning. Center for Curriculum Redesign. URL: Non disponible
  23. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533
  24. Homri, L., & Yaakoubi, A. (2026). Compétences transversales à l’ère de l’intelligence artificielle : Créativité, pensée critique et résolution de problèmes chez les apprenants marocains. Revue de l’École Supérieure de l’Éducation et de la Formation, (7), 113-141. https://doi.org/10.71895/PRSM/revue-rise.n7.120
  25. Hutson, J., et al. (2022). Artificial Intelligence and the Disruption of Higher Education: Strategies for Integrations across Disciplines. Creative Education, 13(12), 3953-3980. https://doi.org/10.4236/ce.2022.1312253
  26. Jose, B., et al. (2025). The cognitive paradox of AI in education: between enhancement and erosion. Frontiers in Psychology, 16, 1550621. https://doi.org/10.3389/fpsyg.2025.1550621
  27. Kasneci, E., et al. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  28. Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press. URL: Non disponible
  29. Kolb, B., & Gibb, R. (2011). Brain plasticity and behaviour in the developing brain. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20(4), 265–276. URL: Non disponible
  30. Kosmyna, N., et al. (2025). Votre Cerveau Sous ChatGPT : Accumulation de dette cognitive lors de l’utilisation d’un assistant d’IA pour la rédaction d’essais. alphaXiv / MIT Media Lab. https://www.alphaxiv.org/abs/2404.19699
  31. Krämer, W. (2014). Kahneman, D. (2011) : Thinking, Fast and Slow. Statistical Papers, 55. https://doi.org/10.1007/s00362-013-0533-y
  32. Lahlou, M., Laamiri, K., & El Guennouni, J. (2026). L’intelligence artificielle au service ou au détriment de la pensée critique des étudiants universitaires ? Analyse et évaluation de la stimulation cognitive et de la dépendance. African Scientific Journal, 3(37), 662–682. https://doi.org/10.5281/zenodo.21620843
  33. Lamharzi Alaoui, M., et al. (2026). L’usage de l’intelligence artificielle générative dans les apprentissages universitaires : une revue systématique de la littérature. International Journal of Digitalization and Applied Management (IJDAM), 3(2), 185–200. https://doi.org/10.23882/ijdam.26346
  34. Levy Provençal, M. (2025). Le décrochage cognitif à l’ère de l’IA. Blog Mikiane. https://www.mikiane.com/blog/2025/3/15/lia-et-le-paradoxe-de-lexternalisation-cognitive
  35. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
  36. Macedo, H. dos S., Santos, I. T. F. dos, & Silva, E. L. O. da (2025). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv preprint / Escola Superior de Tecnologia, UEA. URL: Non disponible
  37. Matei, S. A. (2013). The Shallows: What the Internet Is Doing to Our Brains, by Nicholas Carr. The Information Society, 29(2), 130-132. https://doi.org/10.1080/01972243.2013.758481
  38. Michelot, F., Béland, S., & Poellhuber, B. (2025). Mesurer la pensée critique de futurs enseignants : Éléments de validation d’échelles dans trois nations francophones. European Review of Applied Psychology, 75(5), 101110. https://doi.org/10.1016/j.erap.2025.101110
  39. Ododo, E. P., et al. (2024). Artificial intelligence in the classroom: perceived challenges to vocational education student retention and critical thinking in tertiary institutions. American Journal of Interdisciplinary Innovations and Research, 6(9), 30–39. URL: Non disponible
  40. Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
  41. Sanogo, P. M., & Soro, Z. A. (2025). Intelligence artificielle et cognition des apprenants de l’Institut National de la Jeunesse et des Sports de Marcory (Côte d’Ivoire). RASS. Pensées Genre. Penser Autrement, 5(5), 93–106. URL: Non disponible
  42. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19(4), 469–508. https://doi.org/10.1007/s10648-007-9053-4
  43. Shaikh, S., Daudpotta, S. M., & Imran, A. S. (2021). Bloom’s learning outcomes’ automatic classification using LSTM and pretrained word embeddings. IEEE Access, 9, 117887–117909. https://doi.org/10.1109/ACCESS.2021.3106443
  44. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. Science, 333(6043), 776-778. https://doi.org/10.1126/science.1207745
  45. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
  46. Teräs, M. (2022). Neil Selwyn: Education and technology: Key issues and debates. International Review of Education. https://doi.org/10.1007/s11159-022-09971-9
  47. Tlili, A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
  48. UNESCO (2024). Meilleures pratiques d’encadrement de l’IA dans l’éducation en Afrique. UNESCO, Paris. URL: Non disponible
  49. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS), 30, 5998–6008. URL: Non disponible
  50. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. URL: Non disponible

Empreinte Environnementale, Énergie, Eau et Matières Premières

  1. Académie des technologies (2026). Les consommations du numérique en 2030 : électricité, eau et empreinte carbone (dir. J.-C. André et Y. Caseau). Académie des technologies, Paris. URL: Non disponible
  2. Agomuo, O. C., Jnr, O. W. B., & Muzamal, J. H. (2024). Energy-aware AI-based optimal cloud infra allocation for provisioning of resources. 2024 IEEE/ACIS 27th SNPD, IEEE, pp. 269–274. https://doi.org/10.1109/SNPD61259.2024.10673918
  3. Ahmad, Z., Ashfaque, M., & Khan, I. A. (2025). Generative AIs and the Hidden Cost of Intelligence: A Multidisciplinary Review of LLMs Progress, Resource Consumption and Path to a Sustainable Future. Annual Methodological Archive Research Review, 3(9), 84–107. http://amresearchreview.com/index.php/Journal/about
  4. Akpan, M., & Adebayo, A. (2025). Artificial Intelligence and the Dual Paradoxes: Examining the Interplay of Efficiency, Resource Consumption, and Labor Dynamics. arXiv preprint arXiv:2504.10503. https://doi.org/10.48550/arXiv.2504.10503
  5. Allard, L. (2025). Acteurs, formes et arènes d’engagements pour un numérique soutenable. IRCAV-ObsIA-Sorbonne Nouvelle / Université de Lille. URL: Non disponible
  6. Argerich, M. F., & Patiño-Martínez, M. (2024). Measuring and improving the energy efficiency of large language models inference. IEEE Access, 12, 80194–80207. https://doi.org/10.1109/ACCESS.2024.3409745
  7. Assemblée nationale (France) (2026). Coût carbone de l’IA - Communication de Mme Danièle Obono. Rapport d’information, Assemblée nationale, Paris. URL: Non disponible
  8. Autorité de la concurrence (France) (2025). Competition issues surrounding the energy and environmental impact of artificial intelligence (AI). Étude prospective. URL: Non disponible
  9. Belrzaeg, M., Ahmed, A. A., & Alarga, A. S. D. (2025). AI-Driven Optimization of Renewable Energy Grids Enhancing Efficiency and Sustainability. The Open European Journal of Applied Sciences, 24–35. https://easdjournals.com/index.php/oejas/article/view/15
  10. BNP Paribas Asset Management (2026). Water: The Hidden Constraint in the Digital Economy. Environmental Strategies Group. URL: Non disponible
  11. Bordage, F., Derreumaux, A., & Alfonsi, L. (2025). Impacts environnementaux de l’intelligence artificielle dans le monde. Association GreenIT.fr. URL: Non disponible
  12. Cao, Z., Zhou, X., Hu, H., Wang, Z., & Wen, Y. (2022). Toward a systematic survey for carbon neutral data centers. IEEE Communications Surveys & Tutorials, 24(2), 895–936. https://doi.org/10.1109/COMST.2022.3161275
  13. Carroué, L. (2025). L’essor des centres de données : intelligence artificielle, cyberespace, pouvoir et territoire. Géoconfluences. URL: Non disponible
  14. Chen, Y., Gao, H., & Zou, C. (2026). Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting. arXiv preprint arXiv:2604. URL: Non disponible
  15. d’Aramon, I., Ruf, B., & Detyniecki, M. (2024). Assessing carbon footprint estimations of chatgpt. Renewable Energy Resources and Conservation, Springer, pp. 127–133. https://doi.org/10.1007/978-3-031-59005-4_15
  16. De Vries, A. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. URL: Non disponible
  17. Debus, C., Piraud, M., Streit, A., Theis, F. J., & Götz, M. (2023). Reporting electricity consumption is essential for sustainable AI. Nature Machine Intelligence, 5(11), 1176–1178. https://doi.org/10.1038/s42256-023-00750-1
  18. Ding, C., Ke, J., Levine, M., & Zhou, N. (2024). Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale. Nature Communications, 15(1), 5916. https://doi.org/10.1038/s41467-024-50088-4
  19. Dodge, J., Prewitt, T., Tachet Des Combes, R., et al. (2022). Measuring the Carbon Intensity of AI in Cloud Instances. ACM FAccT ’22, pp. 18–35. URL: Non disponible
  20. Elsworth, C., Huang, K., Patterson, D., Schneider, I., Sedivy, R., et al. (2025). Measuring the environmental impact of delivering AI at Google Scale. arXiv:2508.15734. URL: Non disponible
  21. Enki AI (2026). Critical Minerals: The #1 Data Center Risk for 2026. Commercial Intelligence Report, Enki AI. URL: Non disponible
  22. Everman, B., Villwock, T., Chen, D., Soto, N., Zhang, O., & Zong, Z. (2023). Evaluating the carbon impact of large language models at the inference stage. 2023 IEEE IPCCC, pp. 150–157. https://doi.org/10.1109/IPCCC59175.2023.10253886
  23. GI Digital Library / Gesellschaft für Informatik e.V. (2025). The Systemic Environmental Risks of Artificial Intelligence. SERI Project Assessment, Berlin. URL: Non disponible
  24. Great Lakes Commission (2025). Resolution on Water-Energy Nexus for Datacenters, Artificial Intelligence (AI), Quantum, and Semiconductor Infrastructure Development. Duluth, Minnesota. URL: Non disponible
  25. Gupta, S., et al. (2021). Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level. Transportation Engineering, 4, 100064. https://doi.org/10.1016/j.treng.2021.100064
  26. Hajonides, J., McCarthy, J., Koulouri, K., & Camargo, R. (2025). Navigating AI’s Thirst In A Water Scarce World. Nature Finance. URL: Non disponible
  27. Hamels, S., et al. (2021). The use of primary energy factors and CO2 intensities for electricity in the European context-A systematic methodological review and critical evaluation of the contemporary literature. Renewable and Sustainable Energy Reviews, 146, 111182. https://doi.org/10.1016/j.rser.2021.111182
  28. Herrera, M., Xie, X., Menapace, A., Zanfei, A., & Brentan, B. M. (2025). Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty. EarthArXiv Preprint / Journal of Cleaner Production. URL: Non disponible
  29. IEA - International Energy Agency (2025). Energy and AI. Special Report, Paris. URL: Non disponible
  30. INRIA & Ministère de la Transition écologique (2025). Key Challenges in Fostering the Environmental Performance of AI. Position Paper pour le Sommet pour l’action sur l’IA. URL: Non disponible
  31. Kaneko, Y. (2025). Comparing Electricity Consumption Per Use of Blockchain and Generative AI. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3573722
  32. Li, P., Yang, J., Islam, M. A., & Ren, S. (2025). Making AI Less ‘Thirsty’: Uncovering and Addressing the Secret Water Footprint of AI Models. Communications of the ACM, 68(7), 54–61. https://doi.org/10.1145/3724499
  33. Ligozat, A.-L., & De Vries, A. (2024). IA générative : la consommation énergétique explose. Polytechnique Insights. URL: Non disponible
  34. Liu, H., Aljbri, A., Song, J., Jiang, J., & Hua, C. (2021). Research advances on AI-powered thermal management for data centers. Tsinghua Science and Technology, 27(2), 303–314. https://doi.org/10.26599/TST.2021.9010019
  35. Ljungdahl, V., Jradi, M., & Veje, C. (2022). A decision support model for waste heat recovery systems design in Data Center and High-Performance Computing clusters. Applied Thermal Engineering, 201, 117671. https://doi.org/10.1016/j.applthermaleng.2021.117671
  36. Luccioni, A. S., & Hernandez-Garcia, A. (2023). Counting carbon: A survey of factors influencing the emissions of machine learning. arXiv preprint arXiv:2302.08476. https://doi.org/10.48550/arXiv.2302.08476
  37. Luccioni, S., Jernite, Y., & Strubell, E. (2024). Power Hungry Processing: Watts Driving the Cost of AI Deployment?. ACM FAccT ’24, pp. 85–99. URL: Non disponible
  38. Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2023). Estimating the carbon footprint of BLOOM, a 176B parameter language model. Hugging Face / arXiv preprint. URL: Non disponible
  39. Luccioni, S., Trevelin, B., & Mitchell, M. (2024). The Environmental Impacts of AI - Policy Primer. Hugging Face & Obvia. URL: Non disponible
  40. Mozafari, K., et al. (2026). Quantifying and Disclosing the Environmental Footprint of AI in Research: Life Cycle–Informed Framework and Open-Access Calculator Development Study. JMIR AI, 5, e90770. URL: Non disponible
  41. Mytton, D. (2021). Data centre water consumption. npj Clean Water, 4(1), 11. https://doi.org/10.1038/s41545-021-00101-w
  42. Pakula, G. / Projet Celsius (2026). ChatGPT consomme-t-il vraiment plus qu’une recherche Google ?. Analyse technique Celsius. URL: Non disponible
  43. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350. https://doi.org/10.48550/arXiv.2104.10350
  44. Patterson, D., et al. (2022). The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer, 55(7), 18–28. URL: Non disponible
  45. Pimenow, S., Pimenowa, O., & Prus, P. (2024). Challenges of artificial intelligence development in the context of energy consumption and impact on climate change. Energies, 17(23), 5965. https://doi.org/10.3390/en17235965
  46. Plociennik, C., Watjanatepin, P., Van Acker, K., & Ruskowski, M. (2025). Life Cycle Assessment of Artificial Intelligence Applications: Research Gaps and Opportunities. Procedia CIRP, 135, 924–929. URL: Non disponible
  47. ProductivIA (2026). Le coût matériel de l’IA : comment la crise de la RAM frappe nos ordinateurs. Blog ProductivIA. URL: Non disponible
  48. Roucy-Rochegonde, L. de, & Buffard, A. / Ifri (2025). IA, centres de données et énergie : Quelles tendances ?. Notes de l’Ifri, Paris. URL: Non disponible
  49. Shumba, N., Tshekiso, O., Li, P., Fanti, G., & Ren, S. (2024). A water efficiency dataset for African data centers. NeurIPS Workshop / arXiv:2412.03716. URL: Non disponible
  50. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th ACL, pp. 3645–3650. https://doi.org/10.18653/v1/P19-1355
  51. Tao, M., Zhang, T., Xie, X., & Liang, X. (2023). Water footprint modeling and forecasting of cassava based on different artificial intelligence algorithms in Guangxi, China. Journal of Cleaner Production, 382, 135238. https://doi.org/10.1016/j.jclepro.2022.135238
  52. The Cloud Next Door Study / ACM COMPASS (2025). Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities. arXiv:2501.XXXXX. URL: Non disponible
  53. The Shift Project (2025). Intelligence artificielle, données, calcul : quelles infrastructures dans un monde décarboné ?. Rapport final, Paris. URL: Non disponible
  54. Tian, M., & Adriaens, P. (2025). Predictive learning of operational water intensity from corporate financial indicators: Implications for AI-based resource management. Resources, Conservation and Recycling, 220, 108383. https://doi.org/10.1016/j.resconrec.2025.108383
  55. TIME France / UNU-INWEH (2026). L’intelligence artificielle pourrait consommer autant d’eau que 1,3 milliard de personnes d’ici 2030. Article par Simmone Shah. URL: Non disponible
  56. Toshmatova, G. A., et al. (2025). Measuring AI’s Water Footprint: Hidden Impacts of Digital Technology. Web of Scientist: International Scientific Research Journal, 6(11), 178–186. URL: Non disponible
  57. Ville de Demain × Station F (2025). L’Impact Environnemental du Data Center : Énergie, eau, équipements et chaleur fatale. Guide du Data Center Durable. URL: Non disponible
  58. You, J. / Epoch AI (2025). How much energy does ChatGPT use?. Epoch AI Gradient Updates. URL: Non disponible
  59. Zhang, M. (2024). Data Center Water Usage: A Comprehensive Guide. Technical Report. URL: Non disponible

Droit d’Auteur, Réglementation, Éthique et Souveraineté Numérique

  1. ASI / Frédéric (2024). IA Générative et souveraineté de la donnée : défis et solutions. Blog ASI. https://www.asi.fr/blog/ia-generative-souverainete-donnee-defis-solutions
  2. Bouzidi, A. (2024). La souveraineté numérique à l’ère de l’Intelligence Artificielle. FSJES-AGDAL, Université Mohammed V, Rabat. https://www.revues.imist.ma/index.php/RSMA/article/view/bouzidi-ia-souverainete
  3. Branco, A., et al. (2025). Sovereign AI-based Public Services are Viable and Affordable. University of Lisbon & ARTE. https://arxiv.org/abs/2501.XXXXX
  4. Cabinet Philippe Schmitt Avocats (2026). Conflit IA et droits d’auteurs : confrontation Rapport du Parlement, IA Acte et Directive DSM. Analyse juridique. URL: Non disponible
  5. Conseil d’État (France) (2022). Intelligence artificielle et action publique : construire la confiance, servir la performance. La Documentation française. https://www.conseil-etat.fr/publications-colloques/etudes/intelligence-artificielle-et-action-publique
  6. Consortium ArGiMi - BnF & INA (2026). Synthèse du guide juridique sur l’utilisation des données patrimoniales dans le cadre de l’intelligence artificielle générative. France 2030. URL: Non disponible
  7. Debien, U. (2025). Les droits d’auteur dans le cadre de créations assistées par l’IA. Fiche juridique, Cabinet LeStanc Associés. URL: Non disponible
  8. F.BOX / Proces.fr (2025). Procès opposant The New York Times à OpenAI et Microsoft : Un débat juridique crucial sur l’IA et les droits d’auteur. Account of federal hearing. URL: Non disponible
  9. Guèvremont, V., Richer, I., & Varin, C. (2025). Par-delà les politiques culturelles : explorer le potentiel du droit des technologies numériques et des droits humains pour garantir la découvrabilité d’une diversité de contenus culturels en ligne. Lex Electronica, 30(1), 1–32. https://doi.org/10.7202/1123410ar
  10. Jalicot, C. (2025). IA et droit d’auteur : le vrai, le faux et l’incertain. Chronique Numérique & Société, Polytechnique Insights. URL: Non disponible
  11. La Rédaction de la LJA & Pierre Hoffman (2026). IA Act et droit d’auteur : quels risques pour l’entreprise ?. Lamy Liaisons / LJA. URL: Non disponible
  12. Le Lab de la Sécurité sociale (France) (2024). L’intelligence artificielle, souveraineté numérique et AI Act. Dossier de veille thématique. https://www.lab-securite-sociale.fr/dossiers-veille/ia-souverainete-numerique-ai-act
  13. Masseron, P. (2026). IA générative et droit d’auteur : premières décisions et ligne de fracture. BASES N° 445. URL: Non disponible
  14. Maurice, P. (2026). France-Allemagne : dépasser les différends pour renforcer la souveraineté européenne. Briefings de l’Ifri. https://www.ifri.org/fr/publications/briefings-de-lifri/france-allemagne-depasser-differends-renforcer-souverainete
  15. Ministère de la Culture / CSPLA (2026). Rapport de mission relatif au statut des productions de l’intelligence artificielle (par A. Bensamoun & J. Groffe-Charrier). CSPLA. URL: Non disponible
  16. Monino, J.-L. (2026). Vers une intelligence économique hybride : IA, IA générative et gouvernance territoriale. L’exemple du programme IA.RBRE. ISTE OpenScience, 11(3), 22–41. https://doi.org/10.21494/ISTE.OP.2026.1460
  17. Riant, J. (2026). Musique générée par IA et droit d’auteur : quelles règles ?. Analyse juridique, Riant Avocat. URL: Non disponible
  18. Srivastava, S., & Bullock, J. (2024). AI, Global Governance, and Digital Sovereignty. Purdue University & Texas A&M. https://arxiv.org/abs/2410.XXXXX
  19. Thiérache, C., & Lyazidi, A. (2026). La protection par le droit d’auteur des œuvres utilisées à l’ère de l’IA pour préserver le secteur créatif et le pluralisme des médias. Cabinet Alerion. URL: Non disponible

Génie Logiciel, Assistants IA et Qualité du Code

  1. Abe, K. (2026). I can now create project proposals in 10 minutes. But proposals have decreased. Note.com AI Business Strategy. https://note.com/k_abe_raxus/n/n85e374396511?hl=en
  2. Afroz, S., et al. (2026). The Fast and Spurious: Developer Productivity with GenAI. ACM FSE ’26 / arXiv:2510.24265. https://arxiv.org/abs/2510.24265
  3. Anand / Opsera (2026). DORA vs SPACE Metrics: Key Differences, Use Cases, and When to Use Each Framework. Opsera Technical Knowledge Base. https://opsera.ai/blog/dora-vs-space-metrics-key-differences-use-cases-and-when-to-use-each-framework/
  4. Bruneaux, T. / DX (2026). DORA metrics: the complete guide to measuring DevOps performance in the AI era. DX Engineering Research Hub. https://getdx.com/blog/dora-metrics/
  5. Capgemini Research Institute (2025). Generative AI for software engineering. Capgemini Global Solutions Blueprint. https://www.capgemini.com/us-en/solutions/generative-ai-for-software-engineering/
  6. Chavali, S. (2026). The Productivity Paradox of AI-Assisted Software Development: Empirical Evidence, Longitudinal Quality Degradation, and a Formal Cost-Benefit Model. Preprints.org 202606.2174.v1. https://www.preprints.org/frontend/manuscript/7f44b943a5151fbcf6f389e0d15bdb17/download_pub
  7. Giray, G., Demirörs, O., Kalinowski, M., & Méndez, D. (2026). An Empirical Study of Generative AI Adoption in Software Engineering. Journal of Systems and Software / arXiv:2512.23327v2. https://arxiv.org/html/2512.23327v2
  8. GitClear (2024). Coding on Copilot: 2023 Data Suggests Downward Pressure on Code Quality. GitClear Research Insights. https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality
  9. GitClear (2026). The Maintainability Gap: 2026 AI Code Quality Research. GitClear Whitepaper 2026. https://www.gitclear.com/the_ai_code_quality_maintainability_gap
  10. Harding, W. / Alloy.dev & GitClear (2025). AI Copilot Code Quality: Evaluating 2024’s Increased Defect Rate via Code Quality Metrics. AWS / GitClear Research Whitepaper. https://gitclear-public.s3.us-west-2.amazonaws.com/GitClear-AI-Copilot-Code-Quality-2025.pdf
  11. Kanitkar, A. / Larridin (2026). Developer Productivity Benchmarks 2026 | AI-Native Engineering Data. Larridin Productivity Hub. https://larridin.com/developer-productivity-hub/developer-productivity-benchmarks-2026
  12. Lupyrypa, B. / Weave (2026). Combine DORA and SPACE Metrics to Uncover AI Impact. Weave Engineering Blog. https://weaveos.com/blog/combine-dora-and-space-metrics-to-uncover-ai-impact
  13. Ness Digital Engineering (2026). DORA vs SPACE Framework: Which Engineering Metrics Matter Most?. Ness Insights & Thought Leadership. https://www.ness.com/blog/dora-vs-space-framework-engineering-metrics/
  14. Pai, S., & Willems, M. / Capgemini (2026). Capgemini CTO: Engineering is undergoing a foundational re-architecting. QA Financial Review. https://qa-financial.com/capgemini-cto-qa-in-the-driving-seat-as-ai-rewrites-software-engineering/
  15. Parekh, M. / Unico Connect (2026). AI Statistics 2026: Adoption, ROI, and Real-World Impact. Unico Connect Industry Report. https://unicoconnect.com/blogs/ai-statistics-2026
  16. Tiwary, S., & Zhang, J. / Capgemini (2025). Enhancing the software developers’ experience with Gen AI. Capgemini Expert Perspectives. https://www.capgemini.com/insights/expert-perspectives/enhancing-the-developer-experience-with-gen-ai/
  17. Viney, O. (2026). The AI testing paradox: when silicon valley’s saviour become…. Ouray Viney Quality Engineering Blog. https://www.viney.ca/2026/04/27/the-ai-testing-paradox-when-silicon-valley-s-saviour-become/

Cybersécurité, Shadow AI, Deepfakes et Tatouage Numérique (Watermarking)

  1. Alansari, A., & Luqman, H. (2025). Large Language Models Hallucination: A Comprehensive Survey. arXiv preprint / KFUPM. https://arxiv.org/abs/2502.XXXXX
  2. AuthenTech AI (2026). Samsung ChatGPT Leak (2023): 3 Incidents in 20 Days, Explained. AuthenTech AI Report. https://authentech.ai/shadow-ai/samsung-chatgpt-incident/
  3. Cao, L. (2025). Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities. 1st Workshop on GenAI Watermarking, ICLR 2025. https://www.amazon.com/dp/B0F2ZKH2R4
  4. Chervonyi, Y., et al. (2025). Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2. arXiv preprint. https://arxiv.org/abs/2502.03544
  5. DataFence Team (2026). Samsung’s ChatGPT Ban: Source Code Leak Lessons. DataFence Blog. https://www.datafence.ai/blog/samsung-chatgpt-ban-lessons
  6. Dehghani, A., & Saberi, H. (2025). Generating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools. arXiv preprint / IHU. https://arxiv.org/abs/2501.XXXXX
  7. Economic Times EnterpriseAI / FT (2026). AI-generated ‘slop’ floods bug bounty programmes with false reports. Economic Times. https://enterpriseai.economictimes.indiatimes.com/news/industry/surge-of-low-quality-ai-reports-disrupts-bug-bounty-programs/131192729
  8. Ehrenström, Ph. (2025). Les hallucinations de l’IA générative : dérèglement des sens ou logique de l’idée ?. Blog Droit du travail & Protection des données. https://droitdutravailensuisse.com/2025/05/04/les-hallucinations-de-lia-generative-dereglement-des-sens-ou-logique-de-lidee/
  9. Google DeepMind (2024). AI achieves silver-medal standard solving International Mathematical Olympiad problems. DeepMind Blog. https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
  10. GrosNoob.com (2026). Watermarking de l’IA : comment détecter qu’un texte ou une image a été généré par une machine. Guide GrosNoob.com. https://grosnoob.com/watermarking-ia-detecter-contenu-genere-explication/
  11. Huang, M. M.-Y., Lee, C.-W., Raffel, M., & Tang, E.-C. (2026). Attribute-based Undetectable Watermarking for Generative AI Models. Carnegie Mellon University / GitHub. https://github.com/maxraffel/attribute-based-watermarking
  12. Hubinger, E., et al. / Anthropic (2024). Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. arXiv preprint. https://arxiv.org/abs/2401.05566
  13. Lai, Z., Arif, S., Feng, C., Liao, G., & Wang, C. (2025). Enhancing Deepfake Detection: Proactive Forensics Techniques Using Digital Watermarking. Computers, Materials & Continua, 82(1). https://doi.org/10.32604/cmc.2024.059370
  14. Lakshmanan, R. / The Hacker News (2025). Google AI ‘Big Sleep’ Stops Exploitation of Critical SQLite Vulnerability Before Hackers Act. The Hacker News. https://thehackernews.com/2025/07/google-ai-big-sleep-stops-exploitation.html
  15. Lalai, H. N., Ramakrishnan, A. A., Shah, R. S., & Lee, D. (2024). From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for LLMs. ACL Anthology. https://aclanthology.org
  16. MLQ News (2026). Corporate bug bounty schemes strain under wave of AI-generated junk reports. MLQ Agent. https://mlq.ai/news/corporate-bug-bounty-schemes-strain-under-wave-of-aigenerated-junk-reports/
  17. RansomLeak / Dmytro Koziatynskyi (2026). AI Data Leakage. RansomLeak Blog. https://ransomleak.com/blog/ai-data-leakage-employees/
  18. Schlumberger, É. / Leto.legal (2026). AI Act : marquage des contenus IA (article 50, 2026) - Code de bonne conduite sur les contenus générés par IA. Guide juridique Leto.legal. https://www.leto.legal/guides/code-bonne-conduite-contenu-genere-ia
  19. Skeletos IT Services / Pramood Waikar (2026). Samsung Found Out In 20 Days. Are Your Employees Sharing Data On AI?. Skeletos IT. https://skeletos.io/employees-sharing-confidential-data-chatgpt-ai-nda-breach/
  20. Walker, K. / Google Blog (2025). A summer of security: empowering cyber defenders with AI. Google Cybersecurity Updates. https://blog.google/innovation-and-ai/technology/safety-security/cybersecurity-updates-summer-2025/

Économie, Productivité, Marché du Travail et Optimisation des Modèles

  1. Acemoglu, D. (2025). A new look at the economics of AI. MIT Sloan Ideas Made to Matter. https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-economics-ai
  2. Aubry, E. / WorkflowPro (2026). Coût d’une API IA en production : Claude vs OpenAI. Blog WorkflowPro. https://workflowpro.fr/blog/cout-api-ia-production-claude-vs-openai
  3. Baranez, M. / Studeria (2026). Prix des abonnements IA 2026 : Claude, ChatGPT, Gemini. Studeria. https://www.studeria.fr/articles-de-blog/prix-abonnements-ia-2026-claude-chatgpt-gemini
  4. Be My Eyes (2026). Be My AI™: Revolutionizing Visual Assistance for people who are Blind and Low Vision. Be My Eyes Business. https://www.bemyeyes.com/business/bme-ai/
  5. Bonnouvrier, P. / Silkhom (2026). Salaires IA et ML 2026 : Grilles Complètes par Métier. Baromètre Silkhom. https://www.silkhom.com/les-salaires-ia-et-ml/
  6. Briggs, J., & Kodnani, D. / Goldman Sachs Research (2024). AI is showing ‘very positive’ signs of eventually boosting GDP and productivity. Goldman Sachs Insights. https://www.goldmansachs.com/insights/articles/AI-is-showing-very-positive-signs-of-boosting-gdp
  7. Cailliau, S. / Cigref & Sogeti (2026). Think Tank – Do Tank sur l’IA Générative appliquée au domaine de l’Ingénierie Logicielle (3ème édition). Cigref / Sogeti. https://www.cigref.fr/
  8. Clavey, M. (2026). Après 3 ans d’IA générative, un marché de l’emploi des développeurs touché mais pas coulé. Next.ink. https://next.ink/236671/apres-3-ans-dia-generative-un-marche-de-lemploi-des-developpeurs-touche-mais-pas-coule/
  9. Coursera Editorial Staff (2026). Generative AI Impact on Business. Coursera Articles. https://www.coursera.org/articles/generative-ai-impact-on-business
  10. Dictation Direct / Nuance Microsoft (2026). DAX Copilot - AI Medical Scribe | Save $41,400 Annually. Nuance. https://trydax.com/
  11. EY / Ernst & Young LLP (2026). Scaling AI in government: Cost strategies with the FinOps framework. EY Government & FinOps Practice Whitepaper. URL: Non disponible
  12. Foucher, A. / HCSP (2026). Focus - Choisir son travail demain : Être plus libres et indépendants, individuellement et collectivement. Haut-Commissariat à la Stratégie et au Plan. https://www.strategie-plan.gouv.fr/
  13. Free-Work (2025). Développeurs en 2025 : salaires, IA, télétravail… Ce que révèle le dernier rapport Stack Overflow. Free-Work Tech & IT Blog. https://www.free-work.com/fr/tech-it/blog/metiers-it/developpeurs-en-2025-salaires-ia-teletravail-ce-que-revele-le-dernier-rapport-stack-overflow
  14. GamesIndustry.biz (2026). “It’s a totally crazy market” – The seismic impact of the AI boom on video game hardware. IGN Entertainment Inc. URL: Non disponible
  15. Google DeepMind - Merchant, A., & Cubuk, E. D. (2023). Millions of new materials discovered with deep learning. Google DeepMind Blog. https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
  16. Insilico Medicine (2026). Insilico Initiates Phase III Clinical Trial for Rentosertib, Its AI-Empowered TNIK Inhibitor for Idiopathic Pulmonary Fibrosis. Insilico Press Release. https://insilico.com/news/xmjsn4l091-insilico-initiates-phase-iii-clinical-tr
  17. Insilico Medicine / EurekAlert! (2025). Insilico Medicine announces Nature Medicine publication of Phase IIa results evaluating rentosertib…. EurekAlert!. https://www.eurekalert.org/news-releases/1086096
  18. Insilico Medicine / AlphaGalileo (2026). Insilico’s AI-Driven IPF Candidate Rentosertib Shows Potential for Biological Age Reversal, as Assessed by Six Proteomic Aging Clocks. AlphaGalileo. https://www.alphagalileo.org/de-de/Item-Display-de-DE/ItemId/277798
  19. Institut Mines-Télécom (IMT) (2026). Baromètre des métiers de l’industrie et du numérique 2026. IMT. https://www.imt.fr/barometre-des-metiers-de-l-industrie-et-du-numerique-2026/
  20. Institute for Protein Design, Univ. of Washington (2025). RFdiffusion3 now available. IPD Release. https://www.ipd.uw.edu/2025/12/rfdiffusion3-now-available/
  21. Interesting Engineering (2026). The True Cost and Future of AI - Research Report. Interesting Engineering Research Report. URL: Non disponible
  22. Kim, D. / MAKEBOT.AI (2025). McKinsey Report: How Generative AI is Reshaping Global Productivity and the Future of Work. MAKEBOT.AI Blog. https://www.makebot.ai/blog-en/mckinsey-report-how-generative-ai-is-reshaping-global-productivity-and-the-future-of-work
  23. Lonestone (2026). Comparatif LLM 2026 : quel modèle choisir pour votre produit IA. Lonestone SaaS Guide. https://lonestone.io/creer-saas-ia/comparatif-llm-saas
  24. Martinez, S. (2026). Effect of Artificial Intelligence Adoption on Labour Productivity in The United States. Journal of Economics, Univ. of Minnesota. https://stratfordjournalpublishers.org/journals/index.php/journal-of-economics/article/view/2892
  25. McKinsey & Company (2025). AI in the workplace: A report for 2025 (Superagency in the workplace). McKinsey Insights. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
  26. McKinsey & Company - Tinkoff, D., et al. (2026). The State of AI: Global Survey 2026 - On the road to ROI. QuantumBlack / McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
  27. New Atlas - Blain, L. (2023). Deepmind AI tool catapults materials science 800 years into the future. New Atlas. https://newatlas.com/materials/deepmind-ai-crystals/
  28. Noémie / Jobea (2026). Le développeur junior disparaît : comment l’IA change la recherche d’emploi. Jobea Magazine. https://jobea.fr/blogs/le-developpeur-junior-disparait-comment-lia-change-la-recherche-demploi
  29. Numeum & PAC (2025). Tendances du marché du numérique en 2025 : une fin d’année sous le signe d’une légère reprise et d’un retour progressif à la croissance en 2026. Observatoire de conjoncture Numeum. https://numeum.fr/economie-marche/tendances-du-marche-du-numerique-en-2025-une-fin-dannee-sous-le-signe-dune-legere-reprise-et-dun-retour-progressif-a-la-croissance-en-2026/
  30. OPCO Atlas (2024). INTELLIGENCE ARTIFICIELLE - Étude exploratoire sur les tendances de l’IA pour l’évolution des métiers d’Atlas. OPCO Atlas. https://www.opco-atlas.fr/
  31. Papiernik, E. / Apec (2026). La banalisation de l’intelligence artificielle va fortement impacter les cadres. Apec - Tendances Emploi Cadre. https://www.apec.fr/tendances-emploi-cadre/evolution-des-metiers-et-competences/la-banalisation-de-l-intelligence-artificielle-va-fortement-impact-les-cadres.html
  32. Qualitas Funds / Goldman Sachs (2024). AI as a productivity shock: automation, transformation of work and speed of adoption. Qualitas Funds. https://qualitasfunds.com/en/ai-as-a-productivity-shock-automation-transformation-of-work-and-speed-of-adoption/
  33. Savi Software (2026). L’IA change le coût de création des logiciels. Blog Savi Software. https://savibm.com/fr/blog/ai-changing-software-costs/
  34. Toinel, L. / Geeek.org (2026). Claude Code trop cher ? 8 astuces pour optimiser votre consommation de tokens. Geeek.org. https://www.geeek.org/reduire-tokens-claude-code/
  35. Token Optimize (2026). How to Reduce OpenAI and Claude API Token Costs: A Developer’s Guide. Token Optimize. https://www.tokenoptimize.dev/guides/llm-token-optimization-strategies
  36. Workday & Hanover / La Revue du Digital (2025). Les coûts cachés de l’IA : le temps perdu à corriger ses erreurs. La Revue du Digital. https://www.larevuedudigital.com/les-couts-caches-de-lia-le-temps-perdu-a-corriger-ses-erreurs/

Langages et IA

  1. GenerativeModels.ai R&D Team (2025). Benchmarking Performance of LLMs for Coding. GenerativeModels.ai Blog. https://www.generativemodels.ai/blog
  2. Hacker News Discussion (2025). Even in early 2025, LLMs are already the most powerful type inference algorithm…. Y Combinator Hacker News. https://news.ycombinator.com/item?id=43529036
  3. Human Coders News / GitHub Octoverse (2025). L’IA influence le choix des langages : TypeScript dépasse Python et JavaScript sur GitHub. Human Coders News. https://github.blog/ai-and-ml/generative-ai/how-ai-is-reshaping-developer-choice-and-octoverse-data-proves-it/
  4. AXOPEN Tech Podcast (2025). Langages de programmation : quel avenir face à l’IA ?. AXOPEN - Le podcast tech & développement. https://www.youtube.com/watch?v=AXOPEN_Podcast
  5. Glesner, L. / EloNeva (2025). Qualité du code WLangage généré par l’IA : impact des instructions précises. EloNeva Blog & GitHub IA-Windev-2025. https://eloneva.com/blog/qualite-code-wlangage-ia/
  6. Reddit Community Discussion (2024-2025). Static vs Dynamic typing for LLMs?. r/ChatGPTCoding. https://www.reddit.com/r/ChatGPTCoding/comments/1ioi5sg/static_vs_dynamic_typing_for_llms/
  7. Gertlabs / Hacker News Discussion (2025). Surprisingly, LLMs are actually much worse at reasoning in Python than other common languages. Gertlabs Benchmarks & Hacker News. https://news.ycombinator.com/item?id=48103303
  8. Wikipedia Contributors (2026). Type safety (Definitions, Milner 1978, Wright & Felleisen 1994). Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Type_safety
  9. Levy, T. / Brief IA (2026). TypeScript domine GitHub, la vérification devient centrale. Brief IA - Code & Dev. https://www.briefia.fr/code-ia/typescript-domine-github-la-verification-devient-centrale
  10. Brunner, L. / Builder.io (2025). TypeScript vs JavaScript: Why AI Coding Tools Work Better with TypeScript. Builder.io Blog. https://www.builder.io/blog/typescript-vs-javascript