Sep 2026· Humanities and Social Sciences Communications· 0 citations
Abstract
We draw upon our own experience as educators who teach into the transdisciplinary sustainability field of environment and society to start to argue that, paradoxically, to prepare our students for a future in which AI will increasingly disrupt traditional professions, sources of information, and approaches to knowledge, it is incumbent upon educators such as us to ensure learners have the opportunity to develop certain core, critical human abilities. And, in turn, this requires educators to create “AI-proof” assessments that help foster and protect the development of those abilities. Specifically, we argue learner development of what we identify as the “DEEP” capacities – the abilities to
Discern, Engage, Evaluate, Produce
– is essential to building a resilient, restorative, and fair future. By utilizing AI outputs to fulfill learning assessment requirements, however, students undermine their own development of mastery of the high-level DEEP skills essential to thrive in the Anthropocene. And the risk of students allocating these analytic capacities to AI chatbots – instead of actually developing DEEP skills themselves – is particularly dangerous, as the outputs of generative AI are structurally unreliable and lacking in ethical animus attuned to human and planetary survival and flourishing. Accordingly, in addition to offering a critical perspective, we suggest directions forward for educators to craft AI-proof assessments to foster student development of core DEEP skills in a time of social and environmental urgency. This piece joins a growing call from academics disrupting the uncritical adoption of AI technologies in higher education.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026