Assessing the Usability of Artificial Intelligence-Powered Mobile Learning Applications for Teaching the Hausa Language in Nigerian Colleges of Education

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@article{1723360,
      author = {Dr Tukur Muhammad, Umar Isah, Usman Ibrahim Kaoje, Dr Sani Muhammad Sani},
      title = {Assessing the Usability of Artificial Intelligence-Powered Mobile Learning Applications for Teaching the Hausa Language in Nigerian Colleges of Education},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {4117-4125},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723360.pdf},
      abstract = {Despite the rapid global expansion of Mobile-Assisted Language Learning (MALL) and Artificial Intelligence in Education (AIEd), empirical evaluations of AI-driven mobile applications designed for indigenous African languages remain limited. This study evaluated the usability and user experience of AI-powered mobile learning applications used in teaching and learning the Hausa language across selected Colleges of Education in Northern Nigeria. Grounded in the ISO 9241-11 Usability Model and the Technology Acceptance Model (TAM), a mixed-method empirical design combining a cross-sectional survey with task-based usability testing was executed with pre-service teachers (N = 180) and Hausa language lecturers (N = 15). Quantitative evaluation utilized the System Usability Scale (SUS), a structured Usability Dimensions Questionnaire (UDQ) assessing Learnability, Efficiency, Memorability, Error Tolerance, and Satisfaction, along with four controlled interaction tasks involving Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) modules. Results revealed an overall mean SUS score of 64.20 (SD = 12.80), placing current user experience at a marginal level (Grade D). While Learnability scored high (M = 4.12, SD = 0.65), Error Tolerance (M = 2.85, SD = 0.94) and Efficiency (M = 3.42, SD = 0.81) rated lowest, driven by character rendering errors for hooked letters (ɓ, ɗ, ƙ) and miscalibrated ASR pitch detection for Hausa tonal accents. Multiple linear regression indicated that Error Tolerance (β = .424, p < .001) and Efficiency (β = .380, p < .001) significantly predicted overall user satisfaction (R2 = .551). Furthermore, lecturers rated app usability significantly lower than students (t = 3.482, p < .001). The paper provides a evidence-based software framework for developer optimization and curriculum integration in teacher education.},
      keywords = {Mobile-Assisted Language Learning (MALL); Artificial Intelligence in Education (AIEd); Hausa Language Pedagogy; System Usability Scale (SUS); Human-Computer Interaction (HCI); Colleges of Education; Nigeria},
      month = {September},
  }