The International Journal of Technology, Innovation, and Education

ISSN: 2820 – 7521

Contact Us

Volume 4, 2026 – Issue 3

Low-Bandwidth AI Support for Chinese Teaching in Tanzanian Universities: A Design Synthesis

Jivitius Ching’oro Sabatho 1*, Eliya Mbulika Ndiwu 2, Moabu Jimmy Chandafa 3, and Fang Huang 4

1,2,3,4 School of Education, Huazhong University of Science and Technology (HUST), Wuhan, P.R. China

1 Tanzania Institute of Education (TIE), Dar es Salaam, Tanzania

2 Vocational Education and Training Authority (VETA), Tanzania

3 Department of Technical Education, Mbeya University of Science and Technology (MUST), Mbeya, Tanzania

* Corresponding author: sabathojc@gmail.com

DOI: 10.5281/zenodo.23785206

Abstract

Artificial intelligence (AI) can expand oral practice and feedback in language learning, but its value depends on pedagogy, access, teacher capacity, assessment validity and governance. This conceptual design synthesis develops a low-bandwidth AI-supported pathway for beginner-to-intermediate Chinese teaching in Tanzanian universities. A bounded integrative review drew on 31 sources spanning AI-supported language learning, Mandarin pronunciation and speech recognition, listening-speaking assessment, instructional design, and Tanzanian digital-education policy. Evidence was interpreted through communicative can-do outcomes, Technological Pedagogical Content Knowledge, the Technology Acceptance Model, and human-centred AI principles. The synthesis produced six design requirements: pedagogy before platform, an offline-first core, targeted AI use for feedback-intensive practice, integrated listening-speaking tasks, human-in-the-loop assessment, and readiness before scale. These requirements were translated into a six-stage pathway, an oral-performance rubric, a 60-minute lesson shell, workload scenarios and an institutional readiness scorecard. The framework distinguishes direct Chinese-specific evidence from cross-language extrapolation and provides offline substitutes when connected AI is unavailable. It does not claim effectiveness: no field data were collected, and speech-recognition performance for Mandarin spoken with Tanzanian accents remains unestablished. The framework is intended for piloting, local validation and adaptation before scale.

Keywords: Chinese language teaching, artificial intelligence, low-bandwidth learning, speech recognition, language assessment, Tanzania 

PDF

Published

2026/10/31

—

Section

Research Papers

How to Cite (APA)

16359756 {16359756:RTZGXL3U} 1 apa 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%2C%20J.%20C.%2C%20Ndiwu%2C%20E.%20M.%2C%20Chandafa%2C%20M.%20J.%2C%20%26amp%3B%20Fang%2C%20H.%20%282026%29.%20Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis.%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B4%26lt%3B%5C%2Fi%26gt%3B%283%29%2C%20380%26%23×2013%3B404.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho, J. C., Ndiwu, E. M., Chandafa, M. J., & Fang, H. (2026). Low-bandwidth AI support for Chinese teaching in Tanzanian universities: A design synthesis. The International Journal of Technology, Innovation, and Education, 4(3), 380–404. https://doi.org/10.5281/zenodo.23785206

Download Citation: EndNote/Zotero/Mendeley (RIS)

Chicago
MLA
IEEE
Harvard
Vancouver
APA
Chicago
Chicago
MLA
IEEE
Harvard
Vancouver
APA

16359756 {16359756:RTZGXL3U} 1 chicago-author-date 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%2C%20Jivitius%20Ching%26%23×2019%3Boro%2C%20Eliya%20Mbulika%20Ndiwu%2C%20Moabu%20Jimmy%20Chandafa%2C%20and%20Huang%20Fang.%202026.%20%26%23x201C%3BLow-Bandwidth%20AI%20Support%20for%20Chinese%20Teaching%20in%20Tanzanian%20Universities%3A%20A%20Design%20Synthesis.%26%23x201D%3B%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%204%20%283%29%3A%20380%26%23×2013%3B404.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho, Jivitius Ching’oro, Eliya Mbulika Ndiwu, Moabu Jimmy Chandafa, and Huang Fang. 2026. “Low-Bandwidth AI Support for Chinese Teaching in Tanzanian Universities: A Design Synthesis.” The International Journal of Technology, Innovation, and Education 4 (3): 380–404. https://doi.org/10.5281/zenodo.23785206.

16359756 {16359756:RTZGXL3U} 1 modern-language-association 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%2C%20Jivitius%20Ching%26%23×2019%3Boro%2C%20et%20al.%20%26%23x201C%3BLow-Bandwidth%20AI%20Support%20for%20Chinese%20Teaching%20in%20Tanzanian%20Universities%3A%20A%20Design%20Synthesis.%26%23x201D%3B%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%2C%20vol.%204%2C%20no.%203%2C%20Oct.%202026%2C%20pp.%20380%26%23×2013%3B404%2C%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho, Jivitius Ching’oro, et al. “Low-Bandwidth AI Support for Chinese Teaching in Tanzanian Universities: A Design Synthesis.” The International Journal of Technology, Innovation, and Education, vol. 4, no. 3, Oct. 2026, pp. 380–404, https://doi.org/10.5281/zenodo.23785206.

16359756 {16359756:RTZGXL3U} 1 ieee 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%20style%3D%26quot%3Bclear%3A%20left%3B%20%26quot%3B%26gt%3B%5Cn%20%20%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-left-margin%26quot%3B%20style%3D%26quot%3Bfloat%3A%20left%3B%20padding-right%3A%200.5em%3B%20text-align%3A%20right%3B%20width%3A%201em%3B%26quot%3B%26gt%3B%5B1%5D%26lt%3B%5C%2Fdiv%26gt%3B%26lt%3Bdiv%20class%3D%26quot%3Bcsl-right-inline%26quot%3B%20style%3D%26quot%3Bmargin%3A%200%20.4em%200%201.5em%3B%26quot%3B%26gt%3BJ.%20C.%20Sabatho%2C%20E.%20M.%20Ndiwu%2C%20M.%20J.%20Chandafa%2C%20and%20H.%20Fang%2C%20%26%23x201C%3BLow-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%2C%26%23x201D%3B%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%2C%20vol.%204%2C%20no.%203%2C%20pp.%20380%26%23×2013%3B404%2C%20Oct.%202026%2C%20doi%3A%2010.5281%5C%2Fzenodo.23785206.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%20%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
[1]
J. C. Sabatho, E. M. Ndiwu, M. J. Chandafa, and H. Fang, “Low-bandwidth AI support for Chinese teaching in Tanzanian universities: A design synthesis,” The International Journal of Technology, Innovation, and Education, vol. 4, no. 3, pp. 380–404, Oct. 2026, doi: 10.5281/zenodo.23785206.

16359756 {16359756:RTZGXL3U} 1 harvard1 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%2C%20J.C.%20%26lt%3Bi%26gt%3Bet%20al.%26lt%3B%5C%2Fi%26gt%3B%20%282026%29%20%26%23x201C%3BLow-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%2C%26%23x201D%3B%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%2C%204%283%29%2C%20pp.%20380%26%23×2013%3B404.%20Available%20at%3A%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26lt%3B%5C%2Fa%26gt%3B.%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho, J.C. et al. (2026) “Low-bandwidth AI support for Chinese teaching in Tanzanian universities: A design synthesis,” The International Journal of Technology, Innovation, and Education, 4(3), pp. 380–404. Available at: https://doi.org/10.5281/zenodo.23785206.

16359756 {16359756:RTZGXL3U} 1 vancouver-author-date 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%20JC%2C%20Ndiwu%20EM%2C%20Chandafa%20MJ%2C%20Fang%20H.%20Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis.%20The%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%20%5BInternet%5D.%202026%20Oct%2031%3B4%283%29%3A380%26%23×2013%3B404.%20Available%20from%3A%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho JC, Ndiwu EM, Chandafa MJ, Fang H. Low-bandwidth AI support for Chinese teaching in Tanzanian universities: A design synthesis. The International Journal of Technology, Innovation, and Education [Internet]. 2026 Oct 31;4(3):380–404. Available from: https://ijtie.com/wp-content/uploads/2026/10/N40373.pdf

16359756 {16359756:RTZGXL3U} 1 apa 50 default 2861 https://ijtie.com/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22RTZGXL3U%22%2C%22library%22%3A%7B%22id%22%3A16359756%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Sabatho%20et%20al.%22%2C%22parsedDate%22%3A%222026-10-31%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%202%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BSabatho%2C%20J.%20C.%2C%20Ndiwu%2C%20E.%20M.%2C%20Chandafa%2C%20M.%20J.%2C%20%26amp%3B%20Fang%2C%20H.%20%282026%29.%20Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis.%20%26lt%3Bi%26gt%3BThe%20International%20Journal%20of%20Technology%2C%20Innovation%2C%20and%20Education%26lt%3B%5C%2Fi%26gt%3B%2C%20%26lt%3Bi%26gt%3B4%26lt%3B%5C%2Fi%26gt%3B%283%29%2C%20380%26%23×2013%3B404.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.5281%5C%2Fzenodo.23785206%26lt%3B%5C%2Fa%26gt%3B%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Low-bandwidth%20AI%20support%20for%20Chinese%20teaching%20in%20Tanzanian%20universities%3A%20A%20design%20synthesis%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jivitius%20Ching%27oro%22%2C%22lastName%22%3A%22Sabatho%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Eliya%20Mbulika%22%2C%22lastName%22%3A%22Ndiwu%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Moabu%20Jimmy%22%2C%22lastName%22%3A%22Chandafa%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Huang%22%2C%22lastName%22%3A%22Fang%22%7D%5D%2C%22abstractNote%22%3A%22Artificial%20intelligence%20%28AI%29%20can%20expand%20oral%20practice%20and%20feedback%20in%20language%20learning%2C%20but%20its%20value%20depends%20on%20pedagogy%2C%20access%2C%20teacher%20capacity%2C%20assessment%20validity%20and%20governance.%20This%20conceptual%20design%20synthesis%20develops%20a%20low-bandwidth%20AI-supported%20pathway%20for%20beginner-to-intermediate%20Chinese%20teaching%20in%20Tanzanian%20universities.%20A%20bounded%20integrative%20review%20drew%20on%2031%20sources%20spanning%20AI-supported%20language%20learning%2C%20Mandarin%20pronunciation%20and%20speech%20recognition%2C%20listening-speaking%20assessment%2C%20instructional%20design%2C%20and%20Tanzanian%20digital-education%20policy.%20Evidence%20was%20interpreted%20through%20communicative%20can-do%20outcomes%2C%20Technological%20Pedagogical%20Content%20Knowledge%2C%20the%20Technology%20Acceptance%20Model%2C%20and%20human-centred%20AI%20principles.%20The%20synthesis%20produced%20six%20design%20requirements%3A%20pedagogy%20before%20platform%2C%20an%20offline-first%20core%2C%20targeted%20AI%20use%20for%20feedback-intensive%20practice%2C%20integrated%20listening-speaking%20tasks%2C%20human-in-the-loop%20assessment%2C%20and%20readiness%20before%20scale.%20These%20requirements%20were%20translated%20into%20a%20six-stage%20pathway%2C%20an%20oral-performance%20rubric%2C%20a%2060-minute%20lesson%20shell%2C%20workload%20scenarios%20and%20an%20institutional%20readiness%20scorecard.%20The%20framework%20distinguishes%20direct%20Chinese-specific%20evidence%20from%20cross-language%20extrapolation%20and%20provides%20offline%20substitutes%20when%20connected%20AI%20is%20unavailable.%20It%20does%20not%20claim%20effectiveness%3A%20no%20field%20data%20were%20collected%2C%20and%20speech-recognition%20performance%20for%20Mandarin%20spoken%20with%20Tanzanian%20accents%20remains%20unestablished.%20The%20framework%20is%20intended%20for%20piloting%2C%20local%20validation%20and%20adaptation%20before%20scale.%22%2C%22date%22%3A%222026-10-31%22%2C%22section%22%3A%22%22%2C%22partNumber%22%3A%22%22%2C%22partTitle%22%3A%22%22%2C%22DOI%22%3A%2210.5281%5C%2Fzenodo.23785206%22%2C%22citationKey%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Fijtie.com%5C%2Fwp-content%5C%2Fuploads%5C%2F2026%5C%2F10%5C%2FN40373.pdf%22%2C%22PMID%22%3A%22%22%2C%22PMCID%22%3A%22%22%2C%22ISSN%22%3A%222820-7521%22%2C%22language%22%3A%22%22%2C%22collections%22%3A%5B%22QWYM8LHH%22%5D%2C%22dateModified%22%3A%222026-10-30T01%3A16%3A31Z%22%7D%7D%5D%7D
Sabatho, J. C., Ndiwu, E. M., Chandafa, M. J., & Fang, H. (2026). Low-bandwidth AI support for Chinese teaching in Tanzanian universities: A design synthesis. The International Journal of Technology, Innovation, and Education, 4(3), 380–404. https://doi.org/10.5281/zenodo.23785206

References

Bao, W., Yusop, F. D., & Chang, X. (2025). Effects and contributing factors of an automatic speech recognition dialogue platform on international students’ Chinese speaking ability. Discover Computing, 28, Article 265. https://doi.org/10.1007/s10791-025-09787-9

Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6

Contini-Morava, E. (1997). Swahili phonology. In A. S. Kaye (Ed.), Phonologies of Asia and Africa (Vol. 2, pp. 841–860). Eisenbrauns.

Council of Europe. (2020). Common European framework of reference for languages: Learning, teaching, assessment—Companion volume. Council of Europe Publishing. https://www.coe.int/en/web/common-european-framework-reference-languages/cefr-companion-volume-and-its-language-versions

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Du, J., & Daniel, B. K. (2024). Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice. Computers and Education: Artificial Intelligence, 6, 100230. https://doi.org/10.1016/j.caeai.2024.100230

Hamilton, E. R., Rosenberg, J. M., & Akcaoglu, M. (2016). The substitution augmentation modification redefinition (SAMR) model: A critical review and suggestions for its use. TechTrends, 60(5), 433–441. https://doi.org/10.1007/s11528-016-0091-y

Kafyulilo, A., Fisser, P., & Voogt, J. (2016). Factors affecting teachers’ continuation of technology use in teaching. Education and Information Technologies, 21, 1535–1554. https://doi.org/10.1007/s10639-015-9398-0

Kim, S. K., & Webb, S. (2022). The effects of spaced practice on second language learning: A meta-analysis. Language Learning, 72(1), 269–319. https://doi.org/10.1111/lang.12479

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868

Lai, W. Y. W., & Lee, J. S. (2024). A systematic review of conversational AI tools in ELT: Publication trends, tools, research methods, learning outcomes, and antecedents. Computers and Education: Artificial Intelligence, 7, 100291. https://doi.org/10.1016/j.caeai.2024.100291

Landa, E., Zhu, C., & Sesabo, J. (2021). Readiness for integration of innovative teaching and learning technologies: An analysis of meso-micro variables in Tanzanian higher education. International Journal of Educational Research Open, 2, 100098. https://doi.org/10.1016/j.ijedro.2021.100098

Li, Y., & Lee, G. (2021). The effect of perceptual training on teaching Mandarin Chinese tones. In C. Yang (Ed.), The acquisition of Chinese as a second language pronunciation: Segments and prosody (pp. 107–139). Springer. https://doi.org/10.1007/978-981-15-3809-4_5

Liu, J., & Xiao, C. (2021). Tone category learning should serve tone word learning: An experiment of integrating pronunciation teaching in the L2 Chinese curriculum. In C. Yang (Ed.), The acquisition of Chinese as a second language pronunciation: Segments and prosody (pp. 141–162). Springer. https://doi.org/10.1007/978-981-15-3809-4_6

Liu, S. H. J. (2026). Effects of voice-based ChatGPT on Chinese-as-a-second-language learners’ Mandarin pronunciation. International Journal of Applied Linguistics. Advance online publication. https://doi.org/10.1111/ijal.70213

Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535

Ministry of Education, Science and Technology. (2025). National digital education guidelines for universities. Government of the United Republic of Tanzania. https://www.moe.go.tz/

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Mtebe, J. S., & Raisamo, R. (2014). Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania. The International Review of Research in Open and Distributed Learning, 15(1), 249–271. https://doi.org/10.19173/irrodl.v15i1.1687

Ngo, T. T.-N., Chen, H. H.-J., & Lai, K. K.-W. (2024). The effectiveness of automatic speech recognition in ESL/EFL pronunciation: A meta-analysis. ReCALL, 36(1), 4–21. https://doi.org/10.1017/S0958344023000113

Richardson, I. (1956). The role of tone in the structure of Sukuma (Doctoral dissertation, University of London). SOAS Research Online. https://eprints.soas.ac.uk/29083/

Runge, A., Attali, Y., LaFlair, G. T., Park, Y., & Church, J. (2024). A generative AI-driven interactive listening assessment task. Frontiers in Artificial Intelligence, 7, 1474019. https://doi.org/10.3389/frai.2024.1474019

Sabatho, J. C., Huang, F., Mshana, G. G., & Rwiza, F. B. (2026). Sustaining Chinese language curriculum reform in Tanzania: A documentary analysis of policy alignment, teacher support, localisation, and institutional coordination. International Journal of Research and Scientific Innovation, 13(6), 4447–4462. https://doi.org/10.51244/IJRSI.2026.1306000332

Shi, X., Wang, X., & Zhang, W. (2024). Exploring the relationships between ASS indices and CAF and the impact on Chinese college students’ oral English performance. Language Testing in Asia, 14, Article 30. https://doi.org/10.1186/s40468-024-00306-z

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Tanzania Institute of Education. (2023). Chinese language syllabus for ordinary secondary education Form I–IV. Ministry of Education, Science and Technology, United Republic of Tanzania. https://www.tie.go.tz/uploads/documents/sw-1727193068-CHINESE%20LANGUAGE%20SYLLABUS%20FOR%20ORDINARY%20SECONDARY%20EDUCATION%20FORM%20I-IV.pdf

Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606

UNESCO. (2023). Global education monitoring report 2023: Technology in education—A tool on whose terms? UNESCO. https://doi.org/10.54676/UZQV8501

Wu, X.-Y. (2024). Artificial intelligence in L2 learning: A meta-analysis of contextual, instructional, and social-emotional moderators. System, 126, 103498. https://doi.org/10.1016/j.system.2024.103498

Wu, Y., Su, H., & Yin, H. (2025). Enhancing Mandarin tone acquisition: Computer-assisted pronunciation training in self-directed L2 learning. International Journal of Chinese Linguistics, 12(2), 286–314. https://doi.org/10.1075/ijchl.00040.wu

About the authors
  1. Jivitius Ching’oro Sabatho is a PhD candidate in Educational Economy and Management at HUST and Chinese Language Curriculum Developer at the Tanzania Institute of Education. His research focuses on curriculum management, Chinese language education, teacher education, educational policy and AI-supported education.
    sabathojc@gmail.com
    ↩︎
  2. Eliya Mbulika Ndiwu is a PhD candidate in Educational Management at HUST and Principal Vocational Tutor at VETA, Tanzania. His research interests include TVET, educational management, technology-supported learning and workforce skills development.
    ndiwuelliya@gmail.com
    ↩︎
  3. Moabu Jimmy Chandafa is a PhD candidate in Higher Education at HUST and Assistant Lecturer in Technical Education at MUST, Tanzania. His interests include educational technology, teacher professional development, curriculum implementation and educational leadership.
    moabuchandafa95@gmail.com
    ↩︎
  4. Fang Huang is an Associate Professor in the School of Education at HUST. Her research interests include curriculum and instruction, science education and comparative research in primary and secondary education.
    fanghuang@hust.edu.cn ↩︎
DOI

TIE Journal

The International Journal of Technology, Innovation, and Education

Submit

The International Journal of

Technology, Innovation, and Education

Overview
Submission
Submission Guidelines
Current Issue
Archives

Contact

contact@ijtie.com