Yemen Science يمن ساينس: الشبكة اليمنية للعلوم والبيئة، موقع يهتم بأخبار العلوم والتكنولوجيا والصحة والبيئة والسكان
مقالات مشابهة
Report – Yemen Science
By Abdulrahman Abotaleb
Research on Yemen’s higher education system points to a chain of obstacles — from weak digital foundations and limited institutional planning to unresolved legal questions — that could determine whether universities can make meaningful use of artificial intelligence.
Artificial intelligence promises to change how universities teach students, conduct research and run their institutions. But for Yemen’s universities, four studies suggest the more immediate challenge lies not with AI itself, but with what needs to be in place before it can be used effectively.
Taken together, the research identifies a series of connected gaps: limited digital transformation, weak integration of AI into university planning and performance measurement, the need for institutional models to guide implementation, and an underdeveloped legal framework for dealing with issues such as privacy, bias and accountability.
The studies use different methods and examine different parts of the higher education system. They do not provide a comprehensive assessment of every university in Yemen. But read collectively, they point to a broader conclusion:
Adopting AI is not simply a technological upgrade. It is an institutional transformation. For Yemeni universities, that transformation may have to begin long before the algorithm.
A digital foundation still under construction
One of the broadest assessments comes from researcher Muwafiq Al-Buraq, who examined digital transformation and AI technology in higher education.
His study reviewed 40 previous research papers and reports from international workshops and drew on China’s experience for comparison with the Yemeni context.
Its assessment was stark: digital transformation and AI technology in Yemen’s higher education institutions remain at a very low level. The study goes further in its conclusions, describing digital transformation as almost non-existent and saying that what has been implemented remains minimal and lacks a clear standard for evaluation.
That finding is notable because the study does not describe a country in which digital transformation has gone entirely undiscussed. It points to conferences on e-learning and digital transformation that produced recommendations for decision-makers and educational institutions, while arguing that these recommendations were not followed by noticeable implementation.
This creates the first major gap emerging from the research:
the distance between recognising digital transformation as a priority and turning that recognition into institutional practice.
Al-Buraq argues that effective use of AI should be preceded by broader digital transformation, including the infrastructure required to support it.
That order matters. AI systems can potentially analyse student performance, identify learners who need additional support, assist with grading and scheduling, personalise learning and automate administrative tasks. But many of those applications depend on something less visible: reliable digital systems, usable data and institutions capable of managing them.
The question, then, quickly moves from technology to planning.
AI ambitions that are difficult to measure
Researcher Anis Awad Ashour Bajubair looked for evidence of that planning in the strategic and operational machinery of universities themselves.
Using a descriptive-analytical approach, Bajubair examined relevant literature and documents and studied a sample of two public Yemeni universities. His focus was the relationship between artificial intelligence and the performance indicators universities use to translate strategies into measurable outcomes.
The study found a low level of integration between AI-related performance indicators and the strategic plans, operational plans and annual reports it examined. It also reported an absence of specialised ministry standards or requirements encouraging universities to develop such measures.
The finding exposes a less obvious barrier to AI adoption.
A university may declare that artificial intelligence is important. But without specific objectives and indicators, it becomes difficult to determine what adoption actually means, whether progress is occurring or whether investments are producing useful results.
Bajubair recommends incorporating performance indicators into strategic plans, translating them into operational plans over defined periods and dedicating some of those indicators specifically to universities’ response to AI. He also calls for policies, regulations and specialist oversight to support planning and evaluation.
In that sense, digital readiness is not only about servers, networks and software. It is also about whether institutions can set goals and measure what happens next.
From diagnosis to a possible model
A third study moves closer to the question of how such a transformation might be organised.
Researchers Abdalmilk Mohammed Yahya Shaker of Sa’adah University and Al-Abass Munther Wardi Al-Alousi of Thamar University developed a proposed framework for digital transformation in postgraduate programmes, using Sa’adah University as a model.
They employed the Delphi method, which seeks to develop expert consensus through successive rounds of consultation. Their proposal was presented to specialists from Yemeni universities and revised following their feedback, with agreement reached in the second round.
Their conception of digital transformation extends beyond introducing new equipment. It includes shifting university operations from traditional to digital systems, developing digital educational programmes and training academic staff, employees and students to work with modern technologies.
The proposed model is intended to improve universities’ readiness for digital transformation in postgraduate education and provide supporting digital infrastructure.
This is where the four studies begin to fit together more clearly.
What Al-Buraq identifies as a weak digital starting point, and Bajubair detects as a gap in planning and measurement, Shaker and Al-Alousi approach as an institutional design problem: how can a university organise the transition?
But even a university with infrastructure, plans and trained staff faces another question once AI begins making its way into real educational decisions.
Who is responsible when AI gets it wrong?
Researchers Fathy Abdulrahman Al-Showaiter and Adel Ahmed Al-Afiri examined the legal challenges surrounding the use of AI in Yemeni higher education.
Their descriptive and analytical study identifies four central concerns: legal responsibility, privacy, bias and safety. These risks are closely connected to the same applications that make AI attractive.
A system capable of analysing student data could help personalise education, for example, while simultaneously raising questions about who can access those data and how they are protected. An algorithm might assist with assessment or decision-making but could also reproduce bias. And as automated systems assume a greater role, responsibility for harmful or incorrect decisions may become harder to assign.
Al-Buraq’s research raises similar concerns, including privacy, student data security, algorithmic bias, facial recognition and classroom surveillance.
Al-Showaiter and Al-Afiri argue that Yemen needs explicit legislation defining responsibilities associated with AI in higher education and setting clearer boundaries for its use.
The implication is significant:
Greater technological capacity creates a corresponding need for stronger governance.
A university cannot become “AI-ready” merely by becoming more digital. It must also decide how the technology can be used, how people affected by it are protected and who remains accountable for its decisions.
Four papers, one larger pattern
None of the four studies alone establishes a national roadmap for AI in Yemeni higher education. Together, however, they reveal a striking sequence.
The first problem is the digital foundation on which advanced technologies depend. The second is institutional planning and measurement, without which ambitions are difficult to translate into accountable action. The third is implementation, including infrastructure, training and organisational change. Running alongside all three is governance, including privacy, safety, fairness and legal responsibility.
That sequence is an interpretation that emerges from reading the studies together, rather than a conclusion claimed by any single paper.
It also points to an important distinction in the debate over AI in education: the difference between having access to artificial intelligence and having an institution capable of using it well.
The former can happen quickly. The latter is much harder.
What the evidence does — and does not — show
The findings should not be treated as a comprehensive measurement of every Yemeni university.
Al-Buraq’s study is based on a descriptive review of previous research, international reports and comparative experience rather than a nationwide field survey. Bajubair examined only two public universities, limiting how far those findings can be generalised.
The Sa’adah University research proposes a framework developed through expert consultation; it does not evaluate a completed digital transformation programme. The legal study is likewise descriptive and analytical rather than an empirical assessment of AI systems already deployed across Yemeni campuses.
Those limitations are important. They mean the four studies cannot establish precisely how digitally prepared Yemen’s entire university system is.
But their different approaches also make their convergence noteworthy.
From technology and planning to postgraduate education and law, the papers repeatedly shift attention away from AI as a standalone tool and towards the institutions expected to use it.
For Yemen’s universities, that may be the more consequential story.
The defining question is not simply whether students and academics will use artificial intelligence. As such technologies become increasingly accessible, some forms of use may be difficult to avoid.
The harder question is whether universities can build the infrastructure, plans, skills, safeguards and accountability mechanisms needed to turn that use into meaningful educational transformation.
The road to AI in Yemen’s universities, the research suggests, does not begin with an algorithm. It begins with building the university that can responsibly use one.
