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Elearning Blog

Terminology drift in multilingual e-learning systems

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Terminology drift rarely appears as an explicit error. Individual terms may be translated correctly in isolation, yet their meaning shifts gradually across modules, languages, and time. In multilingual e-learning systems, terminology functions as connective tissue. It links concepts across lessons, assessments, interfaces, and documentation. When terminology drifts, the system remains operational, but conceptual coherence weakens.
Elearning Blog

How e-learning content degrades after auto-translation

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After auto-translation, most e-learning courses continue to load, run, and complete without visible errors. Navigation works, quizzes can be submitted, and completion states are reached. From a technical perspective, the system appears intact. This is precisely why degradation is difficult to detect. The course does not fail. Instead, instructional effectiveness erodes quietly while the platform continues to operate as designed.
Elearning Blog

When machine translation is sufficient – and when it is not

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Machine translation (MT) is widely used in e-learning contexts to accelerate language conversion. It can generate language variants quickly and at scale. However, the question of whether MT alone is sufficient depends on defined criteria, not on labels such as “internal” or “external”.
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[UPCOMING] 5 – 7 May 2026 – Learntec (Germany)

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Elearning Blog

Terminology drift in multilingual e-learning systems

0
Terminology drift rarely appears as an explicit error. Individual terms may be translated correctly in isolation, yet their meaning shifts gradually across modules, languages, and time. In multilingual e-learning systems, terminology functions as connective tissue. It links concepts across lessons, assessments, interfaces, and documentation. When terminology drifts, the system remains operational, but conceptual coherence weakens.
Elearning Blog

How e-learning content degrades after auto-translation

0
After auto-translation, most e-learning courses continue to load, run, and complete without visible errors. Navigation works, quizzes can be submitted, and completion states are reached. From a technical perspective, the system appears intact. This is precisely why degradation is difficult to detect. The course does not fail. Instead, instructional effectiveness erodes quietly while the platform continues to operate as designed.
Elearning Blog

When machine translation is sufficient – and when it is not

0
Machine translation (MT) is widely used in e-learning contexts to accelerate language conversion. It can generate language variants quickly and at scale. However, the question of whether MT alone is sufficient depends on defined criteria, not on labels such as “internal” or “external”.
E-learning Events

[UPCOMING] 5 – 7 May 2026 – Learntec (Germany)

0
.stk-0fd06a5 {bottom:-14px !important;} .stk-37c799a {min-height:0px !important;max-width:1500px !important;min-width:auto !important;padding-top:0px !important;padding-right:0px !important;padding-bottom:0px...
E-learning Events

[UPCOMING] June 12-13 – EdTech World Forum 2026 (London, UK)

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Terminology drift in multilingual e-learning systems

ELS Authors -
Terminology drift rarely appears as an explicit error. Individual terms may be translated correctly in isolation, yet their meaning shifts gradually across modules, languages, and time. In multilingual e-learning systems, terminology functions as connective tissue. It links concepts across lessons, assessments, interfaces, and documentation. When terminology drifts, the system remains operational, but conceptual coherence weakens.
Read more

How e-learning content degrades after auto-translation

ELS Authors -
After auto-translation, most e-learning courses continue to load, run, and complete without visible errors. Navigation works, quizzes can be submitted, and completion states are reached. From a technical perspective, the system appears intact. This is precisely why degradation is difficult to detect. The course does not fail. Instead, instructional effectiveness erodes quietly while the platform continues to operate as designed.
Read more

When machine translation is sufficient – and when it is not

ELS Authors -
Machine translation (MT) is widely used in e-learning contexts to accelerate language conversion. It can generate language variants quickly and at scale. However, the question of whether MT alone is sufficient depends on defined criteria, not on labels such as “internal” or “external”.
Read more

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