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From Tool Use to Technological Agency: LoopCAT as a Local-First, Open-Source Tool for Translation Technology Education

Gokhan Dogru, Adrià Martín Mor

arXiv:2609.00344v1cs.CL

TL;DR

Translation education must teach both workflow operation and judgement about the technological choices embedded in translation systems. This paper presents LoopCAT and analyzes its implementation, repository history, and verification records as a basis for proposed teaching activities. The result is an inspectable setting for connecting translation decisions with data, interfaces, and software rules, but the educational effects remain untested.

  • Problem

    Translation students need opportunities to connect practical tool use with judgement about quality, data movement, system arrangements, and technological choices.

  • Method

    The paper conducts an artifact-centered design analysis using repository history, implementation inspection, development records, and verification evidence to map LoopCAT capabilities to proposed educational activities.

  • Results

    LoopCAT provides an inspectable local-first CAT setting for teaching how translation decisions interact with data, interfaces, and software rules.

  • Takeaways & Limitations

    The proposed framework organizes participation around operating workflows, evaluating outputs, inspecting and configuring mechanisms, and making or defending bounded interventions.

  • Takeaways & Limitations

    The paper reports no new student-learning outcomes, and its proposed educational benefits require classroom research for validation.

Abstract

from arXiv · show

Translation students need to learn both how to use translation technologies and how to judge the choices those technologies make available. This article presents LoopCAT, an Apache-2.0-licensed, local-first computer-assisted translation environment co-created with OpenAI Codex using GPT-5.5 and GPT-5.6, and proposes a framework connecting workflow competence, evaluative judgement, and technological agency. The account draws on repository history, implementation inspection, and the verification records of an identified development build. LoopCAT combines local project storage, translation memories, terminology, quality assurance, document exchange, and optional connections to local or hosted AI services. Its English, Catalan, and Turkish interface catalogs also make the application itself available as teaching material: students can translate English UI strings into another language, review the existing automatically generated target drafts, import their revisions, and test the interface. We organize these opportunities around four forms of participation: operating a workflow, evaluating outputs, inspecting and configuring mechanisms, and making or defending a bounded intervention. A six-session sequence, a UI-localization assignment, a placeholder example, and an assessment rubric specify how teachers could use the framework. The paper separates implemented capabilities from proposed educational benefits; it reports no new student-learning outcomes. It distinguishes the latest package checks from earlier regression evidence and sets out a protocol for classroom evaluation. LoopCAT provides an inspectable setting for teaching how translation decisions interact with data, interfaces, and software rules. Whether these activities improve judgement, transfer, or participation remains an empirical question.

1. Introduction

Translation technology education must connect operational workflow skills with judgement about technological choices and their consequences. LoopCAT addresses this question through a functioning local-first CAT environment whose proposed educational uses remain distinct from demonstrated learning gains.

  • Using translation tools and judging their technological choices are related but different educational objectives.Students may operate imports, translation-memory matches, or AI suggestions without explaining appropriateness, data movement, or reuse conditions.
  • Fluent machine-generated language requires students to establish quality criteria, examine evidence, compare alternatives, and take responsibility for decisions.They also need to inspect how interfaces organize suggestions, visibility, error categories, connectivity, and external-account requirements.
  • LoopCAT asks how a translation environment can support learning about both translation production and the technological conditions shaping it.Its engineering objective is a dependable editing and delivery workflow; its pedagogical objective is observation, explanation, experimentation, and justified intervention.
  • The article examines a functioning software artifact and proposes teaching activities based on inspecting and changing parts of a translation workflow.It explicitly claims available educational opportunities rather than demonstrated learning gains or a requirement that translators become programmers.
  • The account distinguishes a planned public release from the 31 August development preview and separates refreshed packaging checks from broader earlier verification.It does not report a completed public release.

2. Related work and conceptual foundations

The conceptual foundations connect translation technology competence with evaluative judgement, AI and data literacy, ethical refusal, and bounded technological agency. LoopCAT combines established open-technology precedents with a proposed local CAT workflow for inspection and intervention.

  • Conceptual foundations: Technology is part of professional translation competence but does not replace linguistic judgement, ethical responsibility, or workflow communication.
  • Evaluative judgement: Evaluative judgement requires students to determine the quality of their own and others’ work and make their criteria and reasoning examinable.Related AI-literacy work extends this to evaluating AI outputs and processes, including whether automated assessment deserves confidence.
  • Data and participation: Translation data work frames corpus selection, preparation, and evaluation as forms of translator participation in technological production.These precedents do not establish that every data intervention improves a model or that LoopCAT implements complete training pipelines.
  • Ethical participation: Ethical participation includes defending either adoption or refusal of technology in relation to a brief, evidence, and affected people.Examples include declining to transmit confidential text, rejecting an unsuitable suggestion, or choosing an adequate non-AI workflow.
  • Open technology precedents: LoopCAT brings local CAT workflow, optional AI connections, and editable interface resources into one application while proposing inspection- and intervention-oriented teaching.It does not compare learning outcomes with other CAT, MT, or specialized platforms.
  • Technological agency: The paper defines technological agency as situated capacity to explain, select, configure, challenge, and sometimes alter translation’s technological conditions.Agency depends on knowledge, opportunities, institutional rules, time, hardware, support, and permission to refuse; source-code access alone is insufficient.

3. Materials, method, and scope of the account

The paper is an artifact-centered design analysis of LoopCAT, using repository, implementation, documentation, development-record, and verification evidence to map capabilities to proposed educational consequences. It maintains explicit boundaries between implemented, verified, and proposed claims.

  • Materials and method: The analysis uses repository history, selected commits and tags, documentation, implementation files, verification records, and retained development-task records.Targeted execution of localization modules and a placeholder detector supplemented inspection, while selected records do not constitute a complete development census.
  • Analytic unit: Each design decision or capability is mapped to what students can do, what they can inspect, what educators can assess, and which limitations interrupt the connection.These mappings are interpretive design propositions requiring classroom research rather than evidence of observed student behaviour.
  • Artifact identity: The packaged baseline is LoopCAT 0.0.4-dev.20260831 with 645 fingerprinted source files matching committed source and recorded artifact checksums.The download-record commit preserves source identity, while the verification files specify the exercised scope.
  • Scope of verification: The planned public release must retain an equally explicit record of what was and was not exercised.
  • Claim scope: Implemented, verified, and proposed identify distinct levels of claim throughout the account.Developer involvement creates a risk of favourable selection and interpretation, motivating counterexamples, independent assessment, and publication of teaching materials.

4. Development history as a sequence of educationally relevant choices

LoopCAT’s development history presents successive engineering and co-creation choices as potential teaching material for examining translation workflows, software mechanisms, and human judgement. The account distinguishes documented implementation and verification from proposed educational uses.

  • Historical foundation: The earliest repository records establish an offline translation workflow expanded through interface localization, translation memory, terminology, quality interaction, local MT, and document interchange.These developments brought segmentation, reuse, terminology, review, and export into one connected workflow.
  • Educationally relevant choices: LoopCAT’s shared workflow lets students examine how terminology, memory matches, placeholders, and exported formats can interact across translation decisions.A terminology choice may require revising a memory match, while a fluent target can still damage a placeholder or fail export verification.
  • Engineering choices: The August redesign extracted responsibilities into services and controllers, clarified build boundaries, and separated test machinery from the production renderer.These changes can make traces from interface actions to implementation more feasible, although the composition layer remains complex.
  • Engineering choices: Keyboard and command-palette work treated user control as an engineering concern by avoiding text-input interference and preventing Quick Insert from silently initiating AI requests.The command palette is available through F2 or Ctrl/Cmd+Shift+P.
  • Release identity: Release preparation rebuilt web and Windows artifacts from a shared source snapshot, recorded identities and checksums, and kept historical releases distinct from the current development build.The process verified packaged contents rather than inferring artifact identity from filenames.
  • Release identity: The 31 August packaging refresh produced the current build after removing internal documents and sanitizing workspace references, but its regression evidence remained narrower than the preceding verification.Thirty-seven renderer/runtime files were byte-identical to the preceding verified build, while full regression suites were not repeated.
  • Reproducibility: Versioning and provenance are treated as methodological literacy because teachers, researchers, and students need to connect materials, applications, participants, and translation decisions to identifiable software behavior.This makes release identity relevant to interpreting classroom and research results, not merely to distribution administration.
  • Human–AI co-creation: The Codex co-creation account records planning, bounded delegated work, human evaluation, and correction through use rather than presenting AI contributions as uniformly reliable.A shortcut change required revision after Chrome intercepted bindings, and the developer retained responsibility for accepted changes.

5. Architecture and the current evidence boundary

LoopCAT combines local project state, translation-workflow resources, editing and delivery functions, and optional AI services in a browser and desktop environment. The evidence boundary distinguishes implemented capabilities, recorded checks, and substantial unverified or unsupported scope.

  • Architecture: LoopCAT provides bilingual editing, terminology, quality checks, review, recovery, import/export, IndexedDB storage, optional workspace folders, and English, Catalan, and Turkish interfaces.The browser interface is paired with an Electron desktop package, and portable project packages can be saved where directory access and permission are available.
  • Optional services: The ordinary editing workflow does not require hosted AI, while local and hosted model connections introduce different setup, policy, licensing, and data-flow boundaries.Educators must distinguish the LoopCAT application, endpoint or provider, selected model, and rights associated with model weights and data.
  • Architecture: The explanatory architecture separates translation work, local application state, and optional services so students can investigate which layer is responsible when suggestions, recovery, or requests fail.The three-layer model is explanatory rather than a claim that every implementation module maps neatly to one layer.
  • Human control: LoopCAT supports deliberate insertion, review, correction, undo/redo, bounded target histories, recovery, and export checks, but these mechanisms do not record the entire translation process or assess every aspect of adequacy.Target history retains up to 25 entries per segment, while QA warnings cover only specified conditions.
  • Delivery: Export pathways differ in how they handle structural risks, reviewable problems, untranslated content, and confirmed target text, so users must inspect what each output contains.Monolingual delivery may use confirmed source-text fallback, whereas bilingual interchange can preserve an empty target.
  • Privacy and recovery: Local-first storage still requires backups and data-flow scrutiny because browser data can be removed, disks can fail, and localhost services may forward contextual information to cloud models.Local-first design is not presented as encryption at rest or regulatory compliance.
  • Verification: The earlier verified build recorded 1,389 passing unit tests, eight successful browser-test phases, and 36 passing automated accessibility audits across light and dark themes.These results belong to the identified 0.0.4-dev.20260831+source.92e3832c0146 build and its preserved verification record.
  • Verification: The current privacy rebuild received focused checks for release contracts, build identity, archives, rendered web smoke, Electron integrity, security fuses, and packaged desktop startup.Startup used isolated temporary profiles, renderer sandboxing, and normal and fallback graphics modes; installer and portable payloads matched the verified unpacked application.

6. A pedagogical framework: operate, evaluate, inspect, intervene

The framework connects workflow operation, evaluative judgement, inspection and configuration, and bounded intervention as distinct but connected forms of participation. A six-session module and UI-localization activities turn these forms into assessable learning tasks.

  • Four connected forms of participation: The framework distinguishes operating workflows, evaluating outputs, inspecting mechanisms, and intervening through justified changes.These forms are connected rather than fixed stages, and competence in one does not guarantee competence in another.
  • Operate: Workflow operation requires preserving resources and delivery constraints, recovering work, and explaining the project, memory, termbase, status, and deliverable.A completed sequence of clicks is insufficient without an inspectable, reopenable result.
  • Evaluate: Evaluating decisions requires comparing candidates against criteria and justifying acceptance, revision, or rejection without treating one text as a permanent tool ranking.Candidates may come from student work, translation memory, NMT, or LLM systems.
  • Inspect: Inspection follows observed behaviour to relevant data, settings, or implementation and predicts what would change if a resource or setting changed.A bounded trace through one function can be meaningful without requiring comprehensive programming knowledge.
  • Intervene: Intervention may modify resources, documentation, configuration, issue reports, or code, but its value depends on the problem addressed and evidence of effects.A clearer warning, corrected termbase, or retained safeguard may be preferable to a new feature.
  • Teaching sequence: The proposed six-session sequence moves from workflow recovery through decision comparison, QA investigation, data mapping, bounded improvement, and transfer defence.Activities include portfolio evidence, peer comparison, configuration choices, interface localization, and defence of translation and infrastructure decisions.
  • UI localization: LoopCAT’s interface catalogs support a non-code localization assignment in which students translate or post-edit messages, import catalogs, and test the running interface.Automatically generated Catalan and Turkish catalogs are assessment material rather than reference translations because their generation and review history are undocumented.

7. A protocol for future educational evaluation

The paper proposes a future evaluation protocol because the functioning application and planned activities do not establish educational effectiveness. The protocol emphasizes preregistered decision-quality measures, controlled teaching comparisons, calibrated assessment, version control, and ethical participation.

  • Study aims: A future study should test decision quality, transfer to another tool or task, and technical or institutional barriers rather than infer learning from a functioning application.The paper explicitly identifies the absence of learning outcomes as an unresolved question.
  • Study design: The initial feasibility design uses pre-task, post-task, and delayed transfer tasks with baseline measures of experience, background, language, and hardware access.Decision quality is the proposed primary outcome, while workflow completion and confidence remain distinct secondary outcomes.
  • Comparison: A comparative study should hold text, teaching time, criteria, and candidates as constant while varying access to inspection and intervention activities.The comparison concerns teaching arrangements rather than software brands, with course-group assignment where feasible.
  • Evidence and analysis: Evidence should combine artifacts, rationales, explanations, observations, and interviews, with assessor calibration, independent scoring where practicable, agreement reporting, and contradiction-seeking qualitative analysis.The protocol includes cases where students understand rules without reading code or produce unexplained patches.
  • Reproducibility: Version control should preserve the application build, source fingerprint, environment, materials, resource versions, and external model or metric configuration.Undocumented live services are unsuitable foundations for repeatable tasks, and external tool use should be recorded separately from LoopCAT.
  • Ethics: Research participation must remain separate from course assessment, with equivalent learning and grading opportunities that do not require research data, public accounts, AI purchases, or published work.Consent, identifiable-data access, ethics review, and institutional approval require advance planning.
  • Data protection: Public demonstrations should use synthetic or appropriately licensed materials, while retention, access, anonymization, withdrawal, and external transmission procedures are specified in advance.Local storage contributes to the plan but does not replace broader data safeguards.

8. Discussion: opportunities, tensions, and limits

The discussion presents LoopCAT as an inspectable teaching setting while emphasizing that inspectability requires scaffolding and that agency remains constrained by access, policy, infrastructure, and institutional conditions. The paper’s strongest limits are the absence of classroom evidence and the unresolved costs of participation.

  • Inspectability: The UI-localization exercise connects tool use, inspection, explanation, and testing, making the CAT environment both a working tool and an object of study.Without that connection, repository access can remain another opaque activity.
  • Scaffolding: Educators need maintained code maps, stable projects, clear data-flow accounts, and successful and unsuccessful intervention examples rather than an unbounded source-reading assignment.Bounded traces and non-code routes can distribute participation more fairly.
  • Access and sustainability: Open-source and local-first access do not remove hardware, installation, maintenance, preparation, support, model, institutional, or accessibility barriers.Local LLMs may be impractical on some devices, and unsigned executables or installation restrictions can impede participation.
  • Relational agency: Students’ agency is relational because briefs, assessment systems, institutional policy, and professional power can constrain which technologies they may use or changes they may deploy.The framework therefore includes documenting concerns, communicating requirements, and negotiating conditions.
  • Responsible intervention: AI-generated patches and tests can share mistaken assumptions, so passing tests require code inspection, counterexamples, and testing in use.Open-source interventions can also create maintenance, import/export, security, or privacy problems.
  • Limits: The account cannot establish comparative usability, sustained adoption, improved employment outcomes, superior translation quality, or educational effectiveness.Its documentary reconstruction is selective, the rubric needs validation, and no LoopCAT classroom study is available.
  • Research agenda: The paper offers inspectable activities and an evaluation plan so future research can test usefulness, audience, costs, technical exclusion, and whether simpler activities produce comparable gains.The proposed claims remain open to challenge rather than being treated as demonstrated benefits.

9. Conclusion

The conclusion presents LoopCAT as a proposed framework and inspectable platform for teaching translation technology through operation, evaluation, inspection, and responsible intervention. It stresses that educational effectiveness, transfer, accessibility, and sustainable classroom use remain to be established.

  • Platform development: LoopCAT’s development progressed from sustaining a complete local translation workflow toward broader resources, AI connections, interface work, internal organization, and release verification.The software was co-created with OpenAI Codex through developer-directed planning, implementation, testing, and correction.
  • Framework: The article proposes four connected participation forms—workflow operation, evaluative judgement, inspection and configuration, and responsible intervention—with activities and evidence for each.The framework includes translation, post-editing, interface testing, non-code routes, and no-AI routes.
  • Open questions: Educational effectiveness, transfer, accessibility, and sustainable classroom use require future research evidence.The conclusion marks these as unresolved rather than observed outcomes.
  • Educational ambition: The intended outcome is not universal software development but informed understanding, justified adoption or refusal, better requirements communication, and possible contribution to technology.The platform supports this ambition only when inspectability is paired with dependable workflows, scaffolding, and attention to limits on individual control.

Declarations and availability

LoopCAT is released under Apache 2.0, with a documented development preview and repository records. The authors also describe Codex’s contributions to software development and manuscript preparation.

  • Software and materials: LoopCAT is licensed under Apache 2.0, and its current development preview is identified as 0.0.4-dev.20260831+source.239cf868ce3d.The passage also identifies the canonical repository and associated source and download-record commits.
  • AI-assisted software development: The authors co-created LoopCAT with OpenAI Codex using GPT-5.5 and GPT-5.6, with Codex contributing to design, code, refactoring, tests, and documentation.The development account records model identifiers, reasoning-effort settings, and developer-reported speed choices.
  • AI-assisted manuscript preparation: Codex assisted with inspecting project materials, checking sources, drafting and revising the article, auditing claims, and producing the document.These activities used task-specific skills and subagents.
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