Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities

Joel Sadiki Charo; Kennedy Ochilo Hadullo

doi:doi:10.11648/j.mlr.20261102.16

Research Article |

| Peer-Reviewed

Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities

Joel Sadiki Charo^*

, Kennedy Ochilo Hadullo

Published in Machine Learning Research (Volume 11, Issue 2)

Received: 21 September 2026 Accepted: 8 October 2026 Published: 29 October 2026

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Technology is playing a vital role across various sectors like education, healthcare, agriculture, financial services, employment, governance, and social development. However, we still see significant digital inequalities, especially in underserved communities, particularly in rural and low-income areas. Here, challenges such as limited connectivity, high technology costs, lack of digital skills, language barriers, and inaccessible digital services hinder meaningful participation in the digital economy. This conceptual paper delves into how adaptive Artificial Intelligence (AI) models can boost digital inclusion and support sustainable development in these communities. It brings together insights from literature on artificial intelligence, digital inclusion, adaptive learning, responsible AI, and sustainable development. The paper introduces the Adaptive AI Digital Inclusion and Sustainable Development (AIDISD) framework, which is built around five key dimensions: accessibility, personalization, contextual adaptation, resource efficiency, and community empowerment. It explores potential applications in education, healthcare, agriculture, financial inclusion, employment, and public services. Additionally, the paper highlights challenges related to infrastructure, data scarcity, algorithmic bias, privacy, cybersecurity, affordability, digital literacy, and institutional capacity. The analysis emphasizes that adaptive AI should be viewed as part of a larger socio-technical strategy rather than just a standalone tech solution. For inclusive deployment, we need local participation, suitable infrastructure, responsible data governance, human oversight, and ongoing evaluation. Ultimately, the paper concludes that well-designed adaptive AI systems can foster more inclusive and sustainable digital ecosystems, provided they are affordable, locally relevant, accessible, transparent, and aligned with the Sustainable Development Goals.

Published in	Machine Learning Research (Volume 11, Issue 2)
DOI	10.11648/j.mlr.20261102.16
Page(s)	137-142
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Adaptive Artificial Intelligence, Digital Inclusion, Digital Divide, Sustainable Development, Underserved Communities, Responsible AI, Digital Transformation, Sustainable Development Goals

References

[1]	Deursen, A. J. (7 Septer 2018). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. https://doi.org/10.1177/1461444818797082
[2]	Hacker, J. V. (19 Jan 2011). The Digital Divide as a Complex and Dynamic Phenomenon. http://dx.doi.org/10.1080/01972240309487
[3]	Morr, C. E. (17 September 2024). AI and disability: A systematic scoping review. https://doi.org/10.1177/14604582241285743
[4]	Scheerder, A. (June 2020). Taking advantage of the Internet: A qualitative analysis to explain why educational background is decisive in gaining positive outcomes. https://doi.org/10.1016/j.poetic.2019.101426
[5]	International Telecommunication Union. (2024). Measuring digital development: Facts and Figures 2024. ITU.
[6]	Organisation for Economic Co-operation and Development. (2019). Recommendation of the Council on Artificial Intelligence. OECD.
[7]	United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. United Nations.
[8]	UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization.
[9]	World Bank. (2016). World Development Report 2016: Digital Dividends. World Bank.
[10]	World Bank. (2021). World Development Report 2021: Data for Better Lives. World Bank.
[11]	World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization.
[12]	Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Cite This Article

Plain Text BibTeX RIS

APA Style

Charo, J. S., Hadullo, K. O. (2026). Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Machine Learning Research, 11(2), 137-142. https://doi.org/10.11648/j.mlr.20261102.16

Copy | Download

ACS Style

Charo, J. S.; Hadullo, K. O. Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Mach. Learn. Res. 2026, 11(2), 137-142. doi: 10.11648/j.mlr.20261102.16

Copy | Download

AMA Style

Charo JS, Hadullo KO. Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Mach Learn Res. 2026;11(2):137-142. doi: 10.11648/j.mlr.20261102.16

Copy | Download

@article{10.11648/j.mlr.20261102.16,
  author = {Joel Sadiki Charo and Kennedy Ochilo Hadullo},
  title = {Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities},
  journal = {Machine Learning Research},
  volume = {11},
  number = {2},
  pages = {137-142},
  doi = {10.11648/j.mlr.20261102.16},
  url = {https://doi.org/10.11648/j.mlr.20261102.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20261102.16},
  abstract = {Technology is playing a vital role across various sectors like education, healthcare, agriculture, financial services, employment, governance, and social development. However, we still see significant digital inequalities, especially in underserved communities, particularly in rural and low-income areas. Here, challenges such as limited connectivity, high technology costs, lack of digital skills, language barriers, and inaccessible digital services hinder meaningful participation in the digital economy. This conceptual paper delves into how adaptive Artificial Intelligence (AI) models can boost digital inclusion and support sustainable development in these communities. It brings together insights from literature on artificial intelligence, digital inclusion, adaptive learning, responsible AI, and sustainable development. The paper introduces the Adaptive AI Digital Inclusion and Sustainable Development (AIDISD) framework, which is built around five key dimensions: accessibility, personalization, contextual adaptation, resource efficiency, and community empowerment. It explores potential applications in education, healthcare, agriculture, financial inclusion, employment, and public services. Additionally, the paper highlights challenges related to infrastructure, data scarcity, algorithmic bias, privacy, cybersecurity, affordability, digital literacy, and institutional capacity. The analysis emphasizes that adaptive AI should be viewed as part of a larger socio-technical strategy rather than just a standalone tech solution. For inclusive deployment, we need local participation, suitable infrastructure, responsible data governance, human oversight, and ongoing evaluation. Ultimately, the paper concludes that well-designed adaptive AI systems can foster more inclusive and sustainable digital ecosystems, provided they are affordable, locally relevant, accessible, transparent, and aligned with the Sustainable Development Goals.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities
AU  - Joel Sadiki Charo
AU  - Kennedy Ochilo Hadullo
Y1  - 2026/10/29
PY  - 2026
N1  - https://doi.org/10.11648/j.mlr.20261102.16
DO  - 10.11648/j.mlr.20261102.16
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 137
EP  - 142
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20261102.16
AB  - Technology is playing a vital role across various sectors like education, healthcare, agriculture, financial services, employment, governance, and social development. However, we still see significant digital inequalities, especially in underserved communities, particularly in rural and low-income areas. Here, challenges such as limited connectivity, high technology costs, lack of digital skills, language barriers, and inaccessible digital services hinder meaningful participation in the digital economy. This conceptual paper delves into how adaptive Artificial Intelligence (AI) models can boost digital inclusion and support sustainable development in these communities. It brings together insights from literature on artificial intelligence, digital inclusion, adaptive learning, responsible AI, and sustainable development. The paper introduces the Adaptive AI Digital Inclusion and Sustainable Development (AIDISD) framework, which is built around five key dimensions: accessibility, personalization, contextual adaptation, resource efficiency, and community empowerment. It explores potential applications in education, healthcare, agriculture, financial inclusion, employment, and public services. Additionally, the paper highlights challenges related to infrastructure, data scarcity, algorithmic bias, privacy, cybersecurity, affordability, digital literacy, and institutional capacity. The analysis emphasizes that adaptive AI should be viewed as part of a larger socio-technical strategy rather than just a standalone tech solution. For inclusive deployment, we need local participation, suitable infrastructure, responsible data governance, human oversight, and ongoing evaluation. Ultimately, the paper concludes that well-designed adaptive AI systems can foster more inclusive and sustainable digital ecosystems, provided they are affordable, locally relevant, accessible, transparent, and aligned with the Sustainable Development Goals.
VL  - 11
IS  - 2
ER  -

Copy | Download

Author Information

Joel Sadiki Charo

Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya

Contact Email

http://orcid.org/0009-0009-0398-2242
Kennedy Ochilo Hadullo

Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya

Download PDF

Submit an Article

Sections

Plain Text BibTeX RIS

APA Style

Charo, J. S., Hadullo, K. O. (2026). Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Machine Learning Research, 11(2), 137-142. https://doi.org/10.11648/j.mlr.20261102.16

Copy | Download

ACS Style

Charo, J. S.; Hadullo, K. O. Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Mach. Learn. Res. 2026, 11(2), 137-142. doi: 10.11648/j.mlr.20261102.16

Copy | Download

AMA Style

Charo JS, Hadullo KO. Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities. Mach Learn Res. 2026;11(2):137-142. doi: 10.11648/j.mlr.20261102.16

Copy | Download

@article{10.11648/j.mlr.20261102.16,
  author = {Joel Sadiki Charo and Kennedy Ochilo Hadullo},
  title = {Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities},
  journal = {Machine Learning Research},
  volume = {11},
  number = {2},
  pages = {137-142},
  doi = {10.11648/j.mlr.20261102.16},
  url = {https://doi.org/10.11648/j.mlr.20261102.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20261102.16},
  abstract = {Technology is playing a vital role across various sectors like education, healthcare, agriculture, financial services, employment, governance, and social development. However, we still see significant digital inequalities, especially in underserved communities, particularly in rural and low-income areas. Here, challenges such as limited connectivity, high technology costs, lack of digital skills, language barriers, and inaccessible digital services hinder meaningful participation in the digital economy. This conceptual paper delves into how adaptive Artificial Intelligence (AI) models can boost digital inclusion and support sustainable development in these communities. It brings together insights from literature on artificial intelligence, digital inclusion, adaptive learning, responsible AI, and sustainable development. The paper introduces the Adaptive AI Digital Inclusion and Sustainable Development (AIDISD) framework, which is built around five key dimensions: accessibility, personalization, contextual adaptation, resource efficiency, and community empowerment. It explores potential applications in education, healthcare, agriculture, financial inclusion, employment, and public services. Additionally, the paper highlights challenges related to infrastructure, data scarcity, algorithmic bias, privacy, cybersecurity, affordability, digital literacy, and institutional capacity. The analysis emphasizes that adaptive AI should be viewed as part of a larger socio-technical strategy rather than just a standalone tech solution. For inclusive deployment, we need local participation, suitable infrastructure, responsible data governance, human oversight, and ongoing evaluation. Ultimately, the paper concludes that well-designed adaptive AI systems can foster more inclusive and sustainable digital ecosystems, provided they are affordable, locally relevant, accessible, transparent, and aligned with the Sustainable Development Goals.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Artificial Intelligence Models for Enhancing Digital Inclusion and Sustainable Development in Underserved Communities
AU  - Joel Sadiki Charo
AU  - Kennedy Ochilo Hadullo
Y1  - 2026/10/29
PY  - 2026
N1  - https://doi.org/10.11648/j.mlr.20261102.16
DO  - 10.11648/j.mlr.20261102.16
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 137
EP  - 142
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20261102.16
AB  - Technology is playing a vital role across various sectors like education, healthcare, agriculture, financial services, employment, governance, and social development. However, we still see significant digital inequalities, especially in underserved communities, particularly in rural and low-income areas. Here, challenges such as limited connectivity, high technology costs, lack of digital skills, language barriers, and inaccessible digital services hinder meaningful participation in the digital economy. This conceptual paper delves into how adaptive Artificial Intelligence (AI) models can boost digital inclusion and support sustainable development in these communities. It brings together insights from literature on artificial intelligence, digital inclusion, adaptive learning, responsible AI, and sustainable development. The paper introduces the Adaptive AI Digital Inclusion and Sustainable Development (AIDISD) framework, which is built around five key dimensions: accessibility, personalization, contextual adaptation, resource efficiency, and community empowerment. It explores potential applications in education, healthcare, agriculture, financial inclusion, employment, and public services. Additionally, the paper highlights challenges related to infrastructure, data scarcity, algorithmic bias, privacy, cybersecurity, affordability, digital literacy, and institutional capacity. The analysis emphasizes that adaptive AI should be viewed as part of a larger socio-technical strategy rather than just a standalone tech solution. For inclusive deployment, we need local participation, suitable infrastructure, responsible data governance, human oversight, and ongoing evaluation. Ultimately, the paper concludes that well-designed adaptive AI systems can foster more inclusive and sustainable digital ecosystems, provided they are affordable, locally relevant, accessible, transparent, and aligned with the Sustainable Development Goals.
VL  - 11
IS  - 2
ER  -

Copy | Download