Elastus Mambwe- Department of Media and Communication Studies, University of Zambia, Lusaka, Zambia
This reflection essay explores the integration of Artificial Intelligence (AI) into the teaching hospital model of journalism education in resource-constrained Zambia. As AI transforms creativity and innovation, journalism education in low-resource contexts faces the challenge of preparing students for an AI-enhanced future amid technological limitations. Drawing on the Technology Acceptance Model, this paper argues that successful AI integration depends on faculty and students appreciating both the usefulness and accessibility of these technologies. The paper proposes reimagining journalism education through a mobile-first approach that leverages smartphone-accessible AI applications, interdisciplinary collaboration between journalism departments and technology developers to create contextually aware AI solutions, comprehensive AI literacy training, and the integration of AI tools into journalism clinics to enhance production processes. These strategies can transform resource limitations into catalysts for innovation while addressing the digital divide. The paper also emphasises that AI is not a neutral tool but a biased apparatus requiring decolonised and localised development efforts to ensure ethical and contextually relevant implementation in Global South journalism education.
Introduction
The rapid proliferation of artificial intelligence (AI) in different societal sectors has been unprecedented. Previously, the rapid deployment and widespread use of the internet and other new media technologies, now viewed as traditional digital media, fundamentally shifted how people access, interact with, and share knowledge. However, AI has gone beyond that to transform creativity and innovation, and its influence on everyday activities, such as learning and working, is readily apparent, more so and faster than traditional digital tools ever did (Bowen and Watson, 2024). As the AI revolution intensifies, fields such as journalism find themselves at a crossroads, with the very future of the practice brought into focus. Questions have emerged and continue to linger about how journalism education should respond.
As journalism practice evolves, journalism educators find themselves confronted with the new charge of preparing future journalists for the new ways of practising and the added task of using the same technologies in teaching. Furthermore, journalism educators in resource-constrained environments face another complex test: how to prepare students to work in an AI-enhanced future while practising in contexts with substantial technological limitations.
This reflection explores and interrogates the integration of AI into the teaching hospital model of journalism education in the low-resource context of Zambia. The reflection argues that the various limitations in resource-limited contexts, such as Zambia, can also serve as stimuli that can fuel innovation in teaching processes that combine local knowledge with global technological innovations. The paper also loosely draws on the Technology Acceptance Model (Davis, 1989) to explain how an AI-integrated journalism educational environment can only be successful if faculty members and journalism department students appreciate their usefulness and can find them accessible. With a focus on Generative AI (GenAI), which it takes to mean “computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data” (Feuerriegel et al., 2024), this reflection also posits that AI is not a neutral disembodied tool. Rather, AI must be viewed as a highly biased apparatus that returns the perspectives of the large-language models (LLMs) from which it learns, and educators in contexts such as Zambia must look to decolonised and localised AI development efforts. The proposals in this reflection are primarily aspirational; however, where pilot programs or informal adaptations have been implemented, specific examples are provided to illustrate both the possibilities and constraints in real-world applications.
AI in journalism education: global perspectives and local gaps
The introduction of AI in the Global South represents a complex and varied milieu of potential advantages and significant challenges. Scholarship has shown a duality in these perspectives on AI in education (Bowen and Watson, 2024). For instance, GenAI has been used to analyse and synthesise information, assist educators in research and development, generate ideas in curriculum design and improving learning experiences, aid feedback mechanisms, and assist in developing learning materials, among other benefits (Bosch et al., 2023; Gondwe, 2023; Mizumoto and Eguchi, 2023; Pavlik, 2025). For students, GenAI has enhanced the learning experience due to the quick responsiveness that results in record time (Bosch et al., 2023). At the same time, it has been criticised for how it challenges academic integrity due to potential unethical practices, its known record of sometimes producing false results to fulfil a generation prompt, and the possibility of creating an over-dependence on the various tools to the detriment of skills, creativity and innovation (Chan and Hu, 2023; Sok and Heng, 2024).
In Africa, studies have shown that even though tools such as OpenAI’s ChatGPT and Dall-e, or Google’s Gemini, among others, can significantly enhance journalism by improving efficiency and generating content, their usefulness is often limited by biases and deficiencies in the data and LLMs they are trained on, a lack of representation of local contexts, and infrastructural challenges such as unreliable internet connectivity and limited access to digital technologies (Kothari and Cruikshank, 2022; Gondwe, 2023; Munoriyarwa et al., 2023). With AI becoming increasingly ubiquitous in journalism practice, it is now crucial to scrutinise how journalism education is adapting and evolving in different contexts.
In their review of existing literature on the key concerns about generative AI in journalism education, Demmar and Neff (2023, p. 53) found concerns such as student integrity (ethics and cheating), the need for increased AI literacy, and the incorporation of AI into educational settings, to be prominent among scholars (Kalfeli Naya and Angeli, 2025). Scholars argue that journalism education has been at a critical crossroads since the emergence of AI and that the field must adapt by, for instance, balancing traditional news values with reskilling, reforming multidisciplinary curricula, strengthening industry partnerships, and retraining faculty to prepare students for the new AI-enhanced media landscape (Irfan, 2023; Chen et al., 2024; Dinçer, 2024; Hossain and Wenger, 2024; Husnain et al., 2024; Pavlik, 2025).
Despite the identified challenges, there is an increasing sense of enthusiasm and cautious optimism regarding the potential benefits of AI in journalism education. Evidence from these and other papers suggests that AI tools can enhance various aspects of teaching and learning processes and experiences in the field in multiple ways. Unfortunately, there is a limited body of literature exploring the role of AI in journalism education in the Global South, particularly in resource-constrained environments. Therefore, before we can make any prescriptions on the subject, we must take stock of the context of journalism education in Zambia.
Journalism education in Zambia and the teaching hospital model
Journalism education in Zambia has undergone a significant transformation since the country’s independence in 1964. The field has expanded considerably, particularly in alignment with the growth of Zambia’s media landscape following the reintroduction of pluralist politics and liberal economic policies in 1991. Initially, journalism training in Zambia was primarily offered by the Evelyn Hone College of Arts and Applied Sciences, which continues to award diplomas and certificates. The University of Zambia (UNZA) introduced its journalism programme in 1984. Today, journalism education is delivered by public and private institutions at various levels, from certificates to postgraduate degrees (Mambwe, 2024; Chibbonta et al., 2025). While journalism training has traditionally relied on in-person instruction, the COVID-19 pandemic exposed systemic challenges, particularly in implementing online learning and delivering practical training components (Chibbonta et al., 2022).
Journalism programs in Zambia have been strongly influenced by US libertarian principles, especially its emphasis on journalism’s watchdog function in society (Banda et al., 2007). At UNZA’s Department of Media and Communications Studies (DMCS), the program loosely employs the teaching hospital model, with minor variations, whose core principle is learning by doing, focusing on practical experience, professional mentorship, and innovation. The model is highlighted on three levels: first aid (immediate content creation), clinics (mentorship) and labs (innovation and experimentation) (Newton, 2013). Journalism teaching hospitals incorporate newsroom processes such as pitching, original reporting, writing, editing, and publishing. The “the de facto role of editor-in-chief” is often assumed by a faculty member (Scaccia, 2022, pp. 14, 15). Inspired by teaching hospitals in medical education, this model integrates academic and professional supervision in simulated yet real newsroom environments. Students can engage directly with real-world scenarios, cultivating essential critical thinking, ethical decision-making, and adaptability skills. Critics of the model, such as Mensing and Ryfe (2013), argue that it may reinforce traditional journalism rather than foster innovation. Instead, they advocate for models that emphasise entrepreneurial skills and inclusivity (Valencia-Forrester, 2020). Nevertheless, teaching hospitals continue to be an important part of journalism education worldwide. Journalism departments often find themselves caught between the ‘Scylla and Charybdis’ of rising operational costs and the rapidly evolving media industry, which demands continual adaptation and innovation.
The teaching hospital model at UNZA is articulated through three student-run platforms under the DMCS. These include UNZA Radio, Lusaka Star Online Newspaper, and the Digital Media Hub, a social media-driven news and content channel, which provides practical training for students as they are fully fledged media outlets. Faculty members serve as executive producers and editors, offering guidance along the way. However, the challenges of rising operational costs, the lack of equipment, and increased lecturer overload are observable in the operations of these platforms. For instance, between 2014 and 2015, the Lusaka Star was transformed into an online-only newspaper after it became clear that the department could not afford newsprint and colour printing.
Theoretical and methodological considerations
Theoretically, the Technology Acceptance Model (TAM) offers a valuable framework for explaining the adoption of technologies, such as AI, in various areas, including journalism education. The model, developed by Davis (1989), argues that technology acceptance primarily depends on two factors: perceived usefulness, which explains the degree to which users believe the technology will enhance their performance, and perceived ease of access, which is about how effortless users expect the technology interaction to be. The model helps predict and explain user acceptance (Davis and Granić, 2024). In the context of AI and its integration into journalism education, the perceived usefulness and ease of use influence educators’ adoption of AI tools. In resource-constrained settings, such as Zambia, these factors are crucial as they determine the tools employed in teaching and learning. As a theory, TAM gives us a lens to analyse how Zambian journalism education can navigate structural challenges while fostering meaningful adoption of AI.
With this, the primary question this essay ponders remains: how can journalism education in resource-limited environments such as Zambia successfully incorporate AI, using constraints as drivers for innovative solutions tailored to the context rather than obstacles, while thoughtfully examining biases and neocolonial repercussions in the use of AI? To do this, the reflection employs a practice-based and reflexive analytical approach, drawing on direct experience within Zambian journalism education, particularly at the University of Zambia’s DMCS.
Given the context, background and debates highlighted above, what remains is the proposed rethinking of the traditional teaching hospital model that embraces AI. This approach looks to context-appropriate and more sustainable solutions that leverage available AI resources while working to bridge the resource gaps that characterise elements of the digital divide in a low-resource context. The following constitute the key elements of this proposed approach.
Optimising mobile-first approach with AI integration
The operations of the student labs and production rooms are desktop-centric in terms of hardware. This provision is a necessary university IT service to students, and, in this case, it is vital to mimic the standard newsroom setup. Additional hardware requirements for journalism departments arise with the need for production equipment, as noted above. All this results in significantly higher operational costs. However, the shift to mobile-first access to news, information and other services by users is evidence enough to suggest a mobile-first approach to the teaching hospital model and the integration of AI. At UNZA, lecturers have encouraged a smartphone-friendly approach to news and programme production to mitigate the equipment cost hurdle. For instance, most students working on the Lusaka Star have stated that they write and initially edit on their phones and depend on WhatsApp to reach out to some sources, share edited news stories, and discuss news assignments as a group. The fact that many AI solutions are already smartphone-based supports this pursuit. AI applications and tools, thus, efficiently work with this model with significant ease and accessibility. Smartphone-accessible AI-driven apps such as Otter.ai for transcription and Canva for design would make a significant difference. Resource-conscious approaches are particularly relevant, given Ncube et al.'s (2025) findings that resources pose a substantial obstacle to AI integration in Southern African journalism schools, where access to AI technologies remains a challenge, and many institutions struggle to obtain paid AI tools due to economic limitations.
It then remains for journalism departments to find and integrate low-cost, low-bandwidth AI solutions that can be easily implemented and accepted in teaching and learning. These tools can be used to support practicals and can be deployed to meet production requirements. AI programs, such as audio transcribers, automated caption generators, or image editors and generators, can offset the cost of purchasing proprietary professional software that most departments in these contexts cannot afford. For instance, in the UNZA case, the purchase of production software, vital for teaching and use in the production clinics and outlets, has been difficult due to the associated costs. Lecturers and students have often explored usable yet non-industry-standard, open-source alternatives and debated the ethical grey area of using pirated copies of industry-standard programs such as the Adobe Creative Suite.
Adopting a mobile-first approach that integrates AI prepares journalism students for the dynamic contemporary media landscape characterised by mobile consumption. This approach also aligns journalism education with current global trends and developments, giving students the hands-on skills and up-to-date technologies necessary to succeed in a tech-driven industry, combining traditional teaching methods with contemporary media practices. While mobile-first approaches are already encouraged at UNZA, it is crucial to acknowledge that interdisciplinary AI tool development remains aspirational.
Interdisciplinary collaboration to develop suitable AI solutions
To make the preceding proposal more achievable in a long-term and sustainable manner, collaboration between journalism schools or departments and technological development entities, such as computer science departments at universities or developer groups, will be essential to create local, proprietary, and contextually aware AI tools and solutions. If well implemented, such tools can help address the goals of the teaching hospital model. Collaborations between journalism faculties and AI development entities, including computer science departments or developer collectives, can bring considerable advantages, an idea proposed by several scholars, including Babacan et al. (2025) and Wenger et al. (2024), among others. For example, it can lead to the development of AI applications that can be customised to local environments, guaranteeing they cater to the respective needs of institutions. Furthermore, collaboration is a cost-effective option, reducing reliance on expensive commercial software and making AI integration accessible to institutions with limited resources. Additionally, these partnerships enable students to develop interdisciplinary skills, promote innovation, and help them prepare for careers in tech-driven sectors. By investing in locally developed tools, universities can adhere to ethical standards rather than rely on pirated software and create opportunities for broader adoption beyond one institution, but to others with similar needs.
Beyond the obvious benefits of such a collaboration, developing local AI solutions can achieve a greater philosophical and even moral goal. Such investment and empowerment best places journalism departments in a position to provide solutions that reflect the principles of decolonisation, inclusivity and narrative ownership, where the current AI model falls short.
A practical illustration of this synergy can be seen in the recent decision by the DMCS to admit visually impaired students, aligning with UNZA’s commitment to inclusive education. Proprietary AI tools — such as text-to-speech software, automated captioning, and adaptive learning interfaces — could significantly enhance accessibility for these and other students with disabilities, ensuring their training meets both academic and industry standards. However, while such AI solutions exist globally, their effectiveness in Zambia may be limited by contextual barriers. For instance, tools designed for blind students in English-language-only contexts may exclude others who are more comfortable with local languages. Similarly, AI trained on Western pedagogical models might overlook culturally relevant media practices in Zambia. This gap presents an opportunity for locally adapted AI solutions—those that integrate Indigenous languages, Zambian journalistic norms, and region-specific accessibility needs—to deliver truly inclusive education.
Empowerment through AI literacy and training
Although having AI technologies and tools is important, it is even more crucial that users are trained. Without training, many of the proposed elements in this reflection would be amiss. Journalism students and staff must be equipped with the skills to build simple AI tools and adopt various contextual arrangements. Training is essential for journalism students and staff in AI technologies to fully leverage the capabilities of AI tools and stay current with the evolving media landscape. Without adequate and relevant training, the benefits of AI tools in journalism education and practice may not be fully appreciated.
Such training can also be provided under the inter-departmental collaborative agreements. Universities can cultivate innovation by enabling educators to develop and implement AI solutions that cater to their local environments, thereby addressing distinct challenges and relevant ethical concerns. For instance, interdisciplinary partnerships between journalism and computer science faculties can enhance this process, guaranteeing that the tools are functional and relevant to their contexts.
In university settings such as UNZA, this can be achieved through workshops, curriculum integration, faculty training, and joint projects that bridge journalism and technology disciplines. Providing accessible resources such as open-source software and mobile-friendly labs can also further support these efforts. Student internships and capstone projects that provide real-world application opportunities, preparing students to thrive in a tech-driven industry, can also be explored to enhance skills. These efforts align journalism education contexts, such as Zambia, with global trends and address modern media needs while ensuring sustainability and relevance.
Enhancing journalism clinics and production processes with AI integration
From a teaching hospital model perspective, AI tools can benefit processes such as production clinics and teaching practicums. For instance, AI-based peer review and feedback systems can be incorporated into news and script development. For the Lusaka Star online newspaper at UNZA, this process is currently handled by lecturers and tutors; however, it can be time-consuming due to the high enrollment numbers in the department. Implementing AI in such a scenario can ease the workload of increasingly overworked staff. At the same time, students can benefit from the increased efficiency and time saved, thereby reducing the time it takes to publish.
AI can also enhance ethical reporting by using AI-developed fact-checking tools or protocols to ensure accuracy in journalism processes. These tools can help identify potential risks and biases, as well as simulate critiques and possible legal or political challenges, which UNZA Radio has faced over the years (Parks and Mukherjee, 2017; Phiri, 2019). AI can also help with mock productions that can be used for review purposes before final recordings or broadcasts. Within the department, students are already using AI to analyse different data types they may need for their reporting, such as statistics, patterns, and social media trends, employing ChatGPT and DeepSeek. Such solutions also help support the goals of the teaching hospital model in a sustainable manner that works well in resource-constrained contexts. Furthermore, the use of ChatGPT and DeepSeek demonstrates an adoption driven by perceived ease and usefulness for student practitioners, illustrating TAM in this context (Davis and Granić, 2024).
AI tools implemented in teaching hospital model journalism clinics and production processes enhance operational efficiency and foster innovation. For example, integrating AI-powered transcription services during mock productions can streamline the review process by quickly generating transcripts for analysis with lecturers and, in the process, offering valuable insights for improvement. With AI integration in editorial systems within the journalism department, faculty member roles can shift to focus more on coordination, monitoring, mentorship, research, and development, not negating their traditional teaching hospital model roles, but rather in a way that evolves them for the better.
Conclusion
This reflection explored and interrogated the integration of AI into the teaching hospital model of journalism education in Zambia. The paper suggested reimagining the model in a way that works for resource-constrained environments. The discussion examined the impact of AI on journalism and journalism education, as well as the challenges posed by the teaching hospital model in low-resource contexts. The paper argued that a mobile-first approach, combined with AI tools, would align with contemporary media consumption patterns while reducing hardware dependencies that are currently prevalent. However, the success of mobile-first approaches also depends on addressing infrastructure limitations and implementing context-appropriate tools.
The discussion also argued for interdisciplinary collaboration in developing relevant AI solutions that respect the principles of decolonisation. Furthermore, AI literacy training was suggested for both faculty and students. At the same time, AI integration in traditional journalism clinics has been shown to potentially enhance learning experiences and operational efficiency in journalism schools or departments.
The framework proposed in this reflection aims to prepare students for the evolving media landscape. The proposals address distinctive challenges of low-resource contexts. They are low-hanging fruit solutions that emerge from the resource limitations in countries such as Zambia. For many of these, all it takes is bold decision-making and insight into what can work in the short term, leading to a more extended-term plan. However, the starting point would be initial buy-in or, in other words, the acceptance of these technologies. From a TAM perspective, these ideas may have to be weighed on utility and user accessibility scales (Davis and Granić, 2024). This task is one that respective journalism schools or departments and students, whether in teaching hospital models or not, will have to do sooner rather than later.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
EM: Writing – review & editing, Writing – original draft.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Correction note
A correction has been made to this article. Details can be found at: 10.3389/fcomm.2025.1710901.
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Keywords: teaching hospital model, artificial intelligence, journalism education, technology acceptance model, Global South, decolonization, Zambia
Citation: Mambwe E (2025) Exploring the integration of artificial intelligence into the teaching hospital model of journalism education in resource-constrained Zambia. Front. Commun. 10:1625763. doi: 10.3389/fcomm.2025.1625763
Edited by:
Ufuoma Akpojivi, University of the Witwatersrand, South AfricaReviewed by:
Agnieszka Marzęda, University of Warsaw, PolandMbongeni Jonny Msimanga, University of Johannesburg, South Africa
Copyright © 2025 Mambwe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Elastus Mambwe, ZWxhc3R1cy5tYW1id2VAdW56YS56bQ==