--- title: "Prototype of a robotic system to assist the learning process of" description: "The authors build a humanoid robot prototype that supports self-study of English by generating text suited to the learner's… 3 references." authors: ["Carlos Morales-Torres", "Mario Campos-Soberanis", "Diego Campos-Sobrino"] year: 2023 venue: "arXiv:2309.11142" source: "https://arxiv.org/abs/2309.11142" doi: "10.48550/arxiv.2309.11142" arxiv: "2309.11142" licence: "CC BY 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/morales-torres-2023-prototype-robotic-system-assist-learning/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/morales-torres-2023-prototype-robotic-system-assist-learning/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2023](https://md.research.elamur.ai/free/en/papers/2023/page/17/) / Prototype of a robotic system to assist the learning process of English language with text-generation through DNN # Prototype of a robotic system to assist the learning process of English language with text-generation through DNN **Authors:** Carlos Morales-Torres, Mario Campos-Soberanis and Diego Campos-Sobrino · **Year:** 2023 · **Venue:** arXiv:2309.11142 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2309.11142) · [PDF](https://arxiv.org/pdf/2309.11142) · [DOI 10.48550/arxiv.2309.11142](https://doi.org/10.48550/arxiv.2309.11142) · [arXiv 2309.11142](https://arxiv.org/abs/2309.11142) · [OpenAlex](https://openalex.org/W4386942137) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary The authors build a humanoid robot prototype that supports self-study of English by generating text suited to the learner's proficiency. With help from an IELTS examiner, they wrote a small corpus graded into three levels (4,785 sentences, about 150,000 words). They trained several next-word predictors in Keras on it: a simple LSTM, a fine-tuned BERT, an encoder-decoder LSTM and a bidirectional LSTM. The text is produced word by word: each prediction is added to the input and the window slides forward. The body was designed with Denavit–Hartenberg kinematics, modelled in Fusion360 and 3D-printed. A Flask service connects it to cloud speech services and to a Gradio tablet interface. The bidirectional LSTM scored best on held-out data, and the authors explain BERT's weaker score by the scarcity of fine-tuning data. A five-day pilot with three learners, scored on the IELTS rubric, showed modest gains in grammar and fluency but none in pronunciation. The study is preliminary. Its value is as a demonstration that a small, level-controlled corpus plus a light recurrent model can drive an interactive tutor in real time. ## Key points - Corpus: 4,785 sentences, roughly 150,000 words, written with an IELTS evaluator and split by level (elemental, pre-intermediate, and a higher tier). Punctuation is used to segment sentences and is then removed. - Test accuracy on next-word prediction (80/20 split, plus 10% validation during training): - Simple LSTM: 80% - fine-tuned BERT: 80% - Encoder-Decoder LSTM: 89% - Bidirectional LSTM: 95%, trained for 20 epochs - Pipeline: - speech capture and cloud transcription; - a Flask endpoint takes the transcription as a seed and returns a 5-word continuation; - the reply is spoken aloud through a speaker; - a Gradio GUI serves users with speech or hearing impairments. - Pilot study: three learners (elementary, pre-intermediate, upper-intermediate), 50 minutes a day for five days. The paper states this as 250 minutes in one place and as "hours" in the conclusion. - Reported average gains: - grammatical range: about 4% - grammatical accuracy: about 4% - fluency: about 3.33% - pronunciation: no change - The validation curves were unstable, which the authors attribute to uneven level mixing within the corpus. ## Interesting ideas - An expert authors the corpus in explicit proficiency tiers, so the generator's output complexity is controlled by which data it learned from rather than by post-hoc filtering; this suits any system that must adapt text to a reader. - With very little in-domain data, a compact recurrent model beat a fine-tuned large transformer, a reminder to always run a cheap baseline before assuming the big model wins. - The authors blame noisy validation curves on mixed difficulty levels inside one training set, which suggests that heterogeneous data strata can destabilise learning. - The before/after scores are broken down by rubric dimension, and one dimension (pronunciation) did not move; that dimension acts as an informal check that the gains are specific rather than general noise. - A single inference service is exposed through two front ends, voice and a tablet GUI, keeping the model independent of the interaction channel and accessible to more users. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (back): Generation from the meaning graph could take a target-level parameter and pick syntactic constructions per tier, checked against small level-graded reference sets. - **I2** (learn): Every evaluation of a gparse or LLM-teacher component should report a lightweight baseline (e.g. the perceptron parser) trained on the same small slice, as a sanity control. - **I3** (learn): Silver treebank data could be tagged by source and register, with training curves tracked per stratum to locate which slice causes instability. - **I4** (answer): School-test evaluations could report per-skill scores and include a skill the change should not affect, as a negative control on claimed improvements. - **I5** (answer): The readers that answer could sit behind one explainable API, with demos (chat, quiz UI) as thin clients that all show the same reasoning trace. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions Also on the byline: [Universidad Politécnica de Yucatán](https://ror.org/00r0j5m90). 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Social Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · 330 works 🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works · [✍️ Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/) · 214 works ## Cite Cite as: Carlos Morales-Torres, Mario Campos-Soberanis and Diego Campos-Sobrino. 2023. Prototype of a robotic system to assist the learning process of English language with text-generation through DNN. arXiv:2309.11142. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{moralestorres2023prototype, title = {{Prototype of a robotic system to assist the learning process of English language with text-generation through DNN}}, author = {Carlos Morales-Torres, Mario Campos-Soberanis and Diego Campos-Sobrino}, year = {2023}, doi = {10.48550/arxiv.2309.11142}, eprint = {2309.11142}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2309.11142} } ``` - [← previous Prosodic realization and interpretation of English imperatives](https://md.research.elamur.ai/free/en/papers/mcclay-2023-prosodic-realization-interpretation-english-imperatives/) - [next → Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/) - [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/17/) ## References 3 references, 3 of them with a card of ours. ↗ leads to the work. 1. 1. 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Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: State of the art, current trends and challenges. CoRR abs/1708.05148 (2017), http://arxiv.org/abs/1708.05148 15. Lai Poh, E.T., Albert, C., Pei-Wen, T., I-Ming, C., Song Huat, Y.: A review on the use of robots in education and young children. Journal of Educational Technology & Society 19(2), 148–163 (2016), http://www.jstor.org/stable/jeductechsoci.19.2.148 16. Melchior, A., Cohen, F., Cutter, T., Leavitt, T.: More than robots: An evaluation of the first robotics competition participant and institutional impacts. In: Brandeis University Center for Youth and Communities Heller School for Social Policy and Management. (2005) 17. Menekse, M., Higashi, R., Schunn, C.D., Baehr, E.: The role of robotics teams collaboration quality on team performance in a robotics tournament. Journal of Engineering Education 106(4), 564–584 (2017) 18. Merity, S., Keskar, N.S., Socher, R.: Regularizing and optimizing LSTM language models. 6th International Conference on Learning Representations, ICLR 2018 -Conference Track Proceedings (2018) 19. Muzaffar, S., Afshari, A.: Short-Term Load Forecasts Using LSTM Networks. Energy Procedia 158, 2922–2927 (2019), https://www.sciencedirect.com/science/article/pii/S1876610219310008 20. Newton, D.P., Newton, L.D.: Humanoid robots as teachers and a proposed code of practice. Frontiers in Education 4, 125 (2019), https://www.frontiersin.org/article/10.3389/feduc.2019.00125 21. Peters, F.: Master thesis: Design and implementation of a chatbot in the context of customer support (2018), http://hdl.handle.net/2268.2/4625 22. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. CoRR abs/1910.10683 (2019), http://arxiv.org/abs/1910.10683 23. Rosvall, E.: Comparison of sequence classification techniques with bert for named entity recognition. Electrical Engineering and computer science faculty (2019) 24. Santhanam, S.: Context based text-generation using LSTM networks. CoRR abs/2005.00048 (2020), https://arxiv.org/abs/2005.00048 25. Topal, M.O., Bas, A., van Heerden, I.: Exploring transformers in natural language generation: Gpt, bert, and xlnet. CoRR abs/2102.08036 (2021), https://arxiv.org/abs/2102.08036 [↗](https://doi.org/10.1145/2506095.2506121) 3. Robotic system to assist the learning of English through DNN 26. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). CoRR abs/1706.03762 (2017), http://arxiv.org/abs/1706.03762 [↗](https://arxiv.org/abs/1706.03762) *Updated 29 October 2026*