LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components. In large-scale routing problems, localized reconstruction reduces the size of each optimization task, but repair regions within the same incumbent can exhibit substantially different structures. One construction rule must therefore compromise across them. In this paper, we propose SpecAHD, a coupled bilevel framework for within-instance specialization. An upper-level search learns where to expose bounded repair regions, while a lower-level search evolves a complementary repertoire of executable heuristics for the induced repair tasks. The upper-level program determines the repair tasks seen by the lower level, while checked repair outcomes determine how upper-level programs are evaluated. The lower-level objective favors heuristics that perform well on average or solve tasks that the current repertoire handles poorly. For the repair tasks induced by a fixed upper-level program and a fixed lower-level candidate pool, this objective is monotone submodular, allowing greedy repertoire selection with a (1-1/e) approximation guarantee. Across four routing problems and multiple LLM backbones, SpecAHD reduces held-out objective cost by up to 57.7% against the strongest competing AHD baseline and outperforms the per-instance baseline envelope on most public instances.
This study aims to examine the phenomenon of hallucinations in large language models (LLMs) within academic contexts, focusing on their manifestations, causes and implications for academic integrity, research quality and responsible artificial intelligence adoption in higher education.
A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. Searches across Scopus, Web of Science and Emerald Insight databases using keywords related to AI hallucination and academic applications, of which 25 peer-reviewed journal articles met the inclusion criteria. Qualitative thematic analysis was performed using NVivo 14 to synthesise evidence on hallucination types, academic applications, impacts and mitigation strategies.
Six recurring types of hallucinations were identified, with fabricated or inaccurate citations emerging as the most prevalent. The findings indicate that hallucinations systematically compromise academic writing quality, distort assessment processes and undermine epistemic trust in scholarly outputs. Variation in hallucination rates across models and disciplines highlights their context-dependent nature. Key contributing factors include probabilistic text generation, limitations in training data, insufficient contextual understanding and the absence of robust verification mechanisms.
It further contributes a structured classification of hallucination types and a multi-layered governance approach to inform institutional policy and responsible AI adoption.
Addressing hallucinations in academic knowledge production is essential for preserving public trust in higher education and safeguarding the societal value of scholarly research.
This study advances existing knowledge by developing an integrated conceptual perspective linking hallucinations to epistemic risk, information integrity and digital trust.
K. Lai, N. Mustaffa, C. Preece et al.· Journal of Science and Techn...· 0 citations