This chapter develops the third scenario, the Platform Marketplace model. This is situated at the intersection of market-led coordination and AI-first integration. In this future, digital platforms unbundle higher education into specialized functions including skill acquisition, credential signaling, socialization, and career placement. These are disaggregated across providers and recombined through algorithmic coordination. Universities shift from orchestrators of integrated educational experiences to content or service partners competing within platform-governed ecosystems. This chapter grounds the scenario in transaction cost economics, information asymmetry theory, network effects, and platform ecosystem theory, explaining how reduced coordination costs make market-based disaggregation economically viable. Two mechanisms dominate: systemic substitution through unbundling, as platforms progressively absorb functions historically bundled within institutions; and automation enabling scale, as near-zero marginal delivery costs drive platform consolidation toward winner-take-most dynamics. The 2035 operating model features global learning marketplaces controlled by a small number of technology firms, modular micro-credentials replacing integrated degrees, faculty labor restructured into star creators and gig workers, and governance exercised through algorithmic management. This chapter identifies deep stratification, data extraction, and labor market segmentation as principal equity risks, then concludes with signposts for monitoring movement toward this scenario.
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
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
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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