To be FAIR or RIGHT? Methodological [R]esearch [I]ntegrity [G]iven [H]uman-facing [T]echnologies using the example of Learning Technologies

• URL: http://arxiv.org/abs/2603.15366v1
• Date: Mon, 16 Mar 2026 14:41:05 GMT
• Title: To be FAIR or RIGHT? Methodological [R]esearch [I]ntegrity [G]iven [H]uman-facing [T]echnologies using the example of Learning Technologies
• Abstract summary: The RIGHT framework is introduced to fill the gap of existing frameworks for the validity aspect.<n>It is based on existing models of simulation research, design-based research, software engineering and empirical social sciences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quality assessment of Research Software Engineering (RSE) plays an important role in all scientific fields. From the canonical three criteria (reliability, validity, and objectivity) previous research has focussed on reliability and the FAIR principles. The RIGHT framework is introduced to fill the gap of existing frameworks for the validity aspect. The framework is constructed using the methods of theory transfer and process modelling. It is based on existing models of simulation research, design-based research, software engineering and empirical social sciences. The paper concludes with two case studies drawn from the field of learning technologies to illustrate the practical relevance of the framework for human-facing RSE.

Related papers

• Personalized Auto-Research: Towards a True AI Co-Scientist [90.01790420664169]
  Given a research goal, state-of-the-art systems remain researcher-agnostic.<n>What counts as novel, valuable, or feasible depends on the researcher.<n>We propose a framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, writing, and review.
  arXiv  Detail & Related papers  (2026-08-14T20:43:27Z)
• ECLAIR: A Causally-Grounded AI Framework for Scientific Discovery in Empirical Software Engineering [51.19819833971252]
  This paper introduces ECLAIR, a causally grounded AI framework that integrates Large Language Models into every stage of the scientific process.<n>ECLAIR treats LLMs as active scientific agents operating under the principles of causal inference.
  arXiv  Detail & Related papers  (2026-08-03T14:47:03Z)
• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration [63.61423859450929]
  This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses.<n>We identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery.
  arXiv  Detail & Related papers  (2026-01-20T18:46:42Z)
• Systematic Framework of Application Methods for Large Language Models in Language Sciences [23.77480663886995]
  Large Language Models (LLMs) are transforming language sciences.<n>Their widespread deployment currently suffers from methodological fragmentation and a lack of systematic soundness.<n>This study proposes two comprehensive methodological frameworks designed to guide the strategic and responsible application of LLMs in language sciences.
  arXiv  Detail & Related papers  (2025-12-10T11:43:17Z)
• Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies [0.0]
  The determination of sample size in qualitative research has traditionally relied on the subjective and often ambiguous principle of data saturation.<n>This study introduces a new, systematic model based on machine learning (ML) to make this process more objective.
  arXiv  Detail & Related papers  (2025-11-02T17:18:51Z)
• A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora [6.384357773998868]
  Sci-OG is a semi-auto-mated methodology for generating research topic.<n>This paper presents Sci-OG, a semi-auto-mated methodology for generating research topic.<n>We evaluate this approach against a range of alternative solutions using a dataset of 21,649 manually annotated semantic triples.
  arXiv  Detail & Related papers  (2025-08-06T08:48:14Z)
• Domain Knowledge in Requirements Engineering: A Systematic Mapping Study [1.0396117988046165]
  Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE)<n>Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE.
  arXiv  Detail & Related papers  (2025-06-25T18:27:51Z)
• Model Reprogramming Demystified: A Neural Tangent Kernel Perspective [49.42322600160337]
  We present a comprehensive theoretical analysis of Model Reprogramming (MR) through the lens of the Neural Tangent Kernel (NTK) framework.<n>We demonstrate that the success of MR is governed by the eigenvalue spectrum of the NTK matrix on the target dataset.<n>Our contributions include a novel theoretical framework for MR, insights into the relationship between source and target models, and extensive experiments validating our findings.
  arXiv  Detail & Related papers  (2025-05-31T16:15:04Z)
• Model-Guided Fieldwork: A Practical, Methodological and Philosophical Investigation in the use of Ethnomethodology for Engineering Software Requirements [0.0]
  This thesis focuses on the use of ethnomethodological fieldwork for the engineering of software requirements. It proposes an approach, dubbed "Model Guided Fieldwork," to support a fieldworker in making observations that may contribute to a technological development process.
  arXiv  Detail & Related papers  (2024-11-14T09:24:56Z)
• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data.<n>This paper thoroughly investigates recent advances in element-wise ZSIR and provides a basis for its future development.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• A Survey of Reasoning with Foundation Models [235.7288855108172]
  Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. We introduce seminal foundation models proposed or adaptable for reasoning. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
  arXiv  Detail & Related papers  (2023-12-17T15:16:13Z)
• Image Quality Assessment in the Modern Age [53.19271326110551]
  This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA) We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. Both hand-engineered and (deep) learning-based methods will be covered.
  arXiv  Detail & Related papers  (2021-10-19T02:38:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.