There is a major change underway on our campuses, but it arrives\nwithout much fanfare. It does not always manifest in the form of new\nbuildings, new laboratories, or new curricula.<\/p>\n
The change emerges more quietly: on students\u2019 laptop screens, at\nlecturers\u2019 desks, in administrative offices, and gradually within\ndecision-making systems. The name of this change is Artificial\nIntelligence (AI).<\/p>\n
Today, AI is no longer merely a seminar topic or a futuristic\ndiscussion theme. It has become part of everyday academic life. Students\nuse it to summarise readings, explain concepts, generate ideas, or draft\ninitial outlines.<\/p>\n
Lecturers utilise it to prepare materials, create questions, or\ndesign feedback. At the institutional level, AI is beginning to be used\nto read learning patterns, accelerate services, and help interpret\ninstitutional data.<\/p>\n
The scale is not insignificant. The HEPI 2025 survey of 1,041\nfull-time undergraduate students showed that 92 per cent of respondents\nhad used at least one AI tool, and 88 per cent used Generative AI such\nas ChatGPT to assist with assessments. At almost the same time, the\nUNESCO 2025 survey, which gathered 400 responses from academic networks\nin 90 countries, indicated that nine out of ten respondents had used AI\nin their professional work.<\/p>\n
However, only 19 per cent stated that their institutions already had\nformal AI policies, while 42 per cent were still in the process of\ndrafting guidelines. In other words, AI adoption is moving very quickly,\nwhile its governance often lags behind. This, in my view, is where the\nmost important questions begin.<\/p>\n
In higher education, the issue does not stop at what AI can do. The\nmore fundamental question is: what must be preserved when AI begins to\nbe used widely?<\/p>\n
That question is important because a campus is not merely a place\nwhere technology is used. A campus is a space where reasoning is formed,\nintegrity is tested, thinking habits are trained, and intellectual\nresponsibility is cultivated. Higher education is not only a place where\nknowledge is produced, but also where values are nurtured.<\/p>\n
Therefore, AI on campus should be positioned as a tool that expands\nhuman capabilities, not replaces human responsibility. It can help\nlecturers identify patterns of student learning difficulties.<\/p>\n
It can help researchers accelerate initial explorations. It can also\nhelp campus leaders understand data more quickly. However, final\ndecisions, moral judgements, and academic responsibility must remain in\nhuman hands.<\/p>\n
Because technology can calculate, but it has no conscience.\nTechnology can provide answers, but it does not bear the ethical\nconsequences of those answers. At that point, humans must not step\nback.<\/p>\n
The problem is that AI is never entirely neutral. It learns from\ndata, and data can be biased. It works with models, and models can be\nflawed. It also often produces outputs that sound convincing, even when\nthe underlying reasoning is fragile. Therefore, the risks of AI on\ncampus are never purely technical. They can extend into areas of ethics,\njustice, privacy, accountability, learning quality, and even trust in\nthe institution.<\/p>\n
Imagine some seemingly simple scenarios. A system helps read\nstudents\u2019 essays but is subtly more favourable to certain writing\nstyles. A predictive model labels a group of students as \u201cat risk,\u201d and\nthat label gradually influences how the institution views them.<\/p>\n
A lecturer uses AI to assist with grading but cannot explain the\nlogic behind the results. Students may become accustomed to obtaining\ninstant answers without sufficiently experiencing the intellectual\nstruggles that are the essence of education.<\/p>\n
These concerns are not empty speculation. In the same UNESCO survey,\none in four respondents stated that their universities had faced ethical\nissues related to AI, from student dependence on AI to authorship\ndisputes and bias in research.<\/p>\n
This is where the problem lies: the main issue is not the existence\nof AI itself, but the possibility that the speed of technology adoption\noutpaces governance maturity.<\/p>\n
Campuses, therefore, cannot settle for merely having access to\ntechnology. Campuses must have direction in using technology. There must\nbe clarity on the purposes for which AI is used, the contexts in which\nit is appropriate, which data may be processed, who is responsible when\nthe system errs, and how its use is monitored over time.<\/p>\n
But governance is not complete with just writing guidelines.\nDocuments are important, but culture is far more determining. Lecturers,\nstudents, and support staff need adequate AI literacy: understanding its\nlimitations, habitually critically checking outputs, being sensitive to\nethics, and disciplined in safeguarding data. Without that, campuses may\nhave good rules on paper but be fragile in daily practice.<\/p>\n
At this point, data discipline becomes extremely important. In the AI\nera, data is not merely raw material for technology, but also an ethical\nissue.<\/p>\n
Campuses need to clearly distinguish which data is safe to process,\nwhich is sensitive, which channels may be used, who has the right to\naccess, and how usage traces are recorded. Once student data, research\ndocuments, or institutional information enters an insecure system, what\nis at stake is not only privacy, but also trust.<\/p>\n
Another often overlooked aspect is justice. Justice in AI is not only\nabout unbiased algorithms, but also about who has access, who is left\nbehind, and whether this technology expands learning opportunities or\ndeepens inequalities. Higher education should be a space for social\nmobility. Therefore, it must not allow new technology to widen old\ndivides.<\/p>\n
In the end<\/p>", "url": "https:\/\/jawawa.id\/newsitem\/when-artificial-intelligence-enters-the-campus-1776315078", "image": "" }, "sponsor": "Okusi Associates", "sponsor_url": "https:\/\/okusiassociates.com" }