Sign language translation (SLT) can help bridge the communication gap between the Deaf and hard-of-hearing community and the general population. Yet progress has been driven largely by a handful of high-resource sign languages, such as German or American Sign Language, supported by large annotated corpora and large computing budgets. Many sign languages, including Bangla Sign Language (BdSL), remain underrepresented because annotated data is scarce and much research assumes access to powerful hardware. In this paper, we share an early-stage adaptation of the gloss-free SpaMo framework for Bangla, called SpaMo-BdSL, and reflect on what we learned while building it under real resource limits. We report two findings that we believe matter for low-resource and underrepresented SLT. First, a small language-specific model (BanglaT5, 247M parameters) matches the translation quality of a much larger multilingual model (mT0-XL, 3.7B parameters) on our Bangla dataset, while being far cheaper to run; for example, it translates the test set in 40 seconds versus 2 minutes 43 seconds for mT0-XL. Second, a set of simple training changes suggested by a large language model (Claude Opus 4.6) allowed us to train the whole system on a single consumer-grade GPU. We argue that the common assumption ”bigger and more multilingual is better” can quietly exclude underrepresented sign languages and the researchers who work on them, and we use our experience to open a discussion on more inclusive, multilingual, and culturally aware SLT.

Large language models (LLMs) are often suggested as tools for grading student writing and giving feedback, but it is unclear how far we can trust them on the separate skills that make up good writing. We compare two LLM graders (Gemini and Llama) with eight professional English teachers on 20 paragraphs written in second-language English by Bengali-speaking undergraduates in Bangladesh, each graded by two teachers, scoring content, organization, vocabulary, and grammar separately as well as the total. We then run a second stage in which teachers read each model’s written explanation, judge whether they agree with it, and rate the feedback. The LLMs match the teachers on the total score, content, and organization, but are unreliable on vocabulary and grammar, where the two models do not even agree with each other. We also show that one low agreement score is misleading: it comes from graders giving very similar marks (score clustering), not from real disagreement. In the second stage, teachers found the AI feedback safe and useful most of the time (≥ 77.5%), with very few made-up errors (2.5%), and preferred Gemini’s stricter grammar score to their own 20% of the time. LLMs can help grade writing, but a teacher should audit their output, with extra care on vocabulary and grammar.