This post is part of Sci-Fi Scenarios, the foresight series on TeachOnline.ca in which leaders in education and technology respond to five “sci-fi–sounding but plausible” AI futures.

We asked contributors to review five AI-driven scenarios for higher education (2025–2035), pick the one they find most compelling and explain why, and then add one future scenario of their own.

Below is the response from Patricia Mangeol (Sandbox Inc./Open University of Catalonia), one of the leaders we invited to comment.

From the list: Patricia Mangeol’s #1 pick

Job Market-Synchronized Learning Pathways are compelling because they sit at the intersection of a data challenge that AI can help solve — tracking the skills employers seek and helping students navigate educational pathways — and a strategic opportunity for higher education to stay relevant by better equipping learners for fast-changing labour markets.

The equity imperative

Aligning learning with economic opportunity is, first and foremost, an equity issue. Higher education graduates enjoy better employment rates and earnings than those without degrees, even if rising AI capabilities may, over time, reduce the premium of holding a degree. Although degrees are not a perfect proxy for skills, as shown in the OECD Survey of Adult Skills, they remain a key gateway to opportunity.

Yet not all youth earn one. Parental education remains a strong predictor of higher education access. For students who may not attend or defer higher education, skills-based learning opportunities aligned with labour market demand can offer viable, well-paid pathways. AI can help make those options more visible, navigable and attainable.

AI in career guidance

Students after high school and adult learners are often overwhelmed by the abundance of available programs and credentials. AI-powered guidance systems could present tailored options — linking learners’ interests and abilities to career paths, and suggesting relevant educational starting points, from full degrees to apprenticeships, certifications or stackable micro-credentials.

Getting the right information to the right learners at the right time remains difficult. Choices are shaped by many factors — family, peers, self-confidence — and not always by clear information that’s both supportive of students’ aspirations and realistic. For instance, PISA data has shown that students’ expectations about their future education are strongly linked to their socio-economic background. AI can support students from early exploration (“What do I want to be when I grow up?” and “What am I good at?”) through navigating educational options, institutional admissions and financial aid — and it may offer the most value to those who don’t have support from their family or social circles.

The role of institutions

Institutions committed to student employability stand to gain from AI-enabled insights into labour market trends and skills needs. With more granular, real-time data, they can rethink what they offer — from fields of study to program modularity and delivery formats — while also building new models of collaboration with employers.

By better understanding economic and labour market needs, institutions can position themselves as strategic partners to government and business — strengthening employment and entrepreneurial pathways for learners, informing institutional research, innovation and commercialization strategies, and driving local and national economic and social impact.

What we should build toward

To realize this scenario, institutions and governments should invest in better labour market intelligence infrastructure, AI-powered guidance tools and interoperable credential systems. Policies should support partnerships between institutions, employers and other stakeholders to turn skills-needs data into relevant educational pathways and turn the maze of educational options into a map where learners can find a meaningful, achievable path toward economic opportunity.

A worthy addition to the list: University-Driven “AI for Good” Models

Brief: Universities push forward a model of AI development and use grounded in public values.

Key features

• Universities shift from a defensive to a proactive stance on AI, recognizing AI’s massive potential benefits and risks. They lead the development of AI infrastructure, applications, purchases and use cases that embed accessibility, equity and public value.
  
• Universities partner with governments, research centres and industry to map the advancement of AI in various domains — using tools like the OECD’s AI Capabilities Scales, the economic and social implications of that progress, and the effectiveness of legal and policy responses to leverage benefits and mitigate risks.
  
• Universities work with governments and research centres to lead the development of AI ethics and governance protocols, helping shape the direction of technological progress so that it’s beneficial to economies and societies.
  
• Universities advance the idea of public AI, collaborating within countries and across borders to share resources, knowledge and staff — reducing redundancy and building open, shared artifacts that serve as digital public goods. They embrace open-source models as a research approach and a strategic lever for innovation.
  
• Universities foster both multidisciplinary and discipline-specific AI literacies, embedding technical and ethical fluency across STEM, social sciences, humanities and professional programs. They ensure the next generation is prepared to succeed in AI-driven labour markets and societies — but also to actively shape and design these AI systems based on human values.

Likelihood

High. This scenario builds on higher education institutions’ strengths in AI research and education, their role as public actors and a growing desire among governments to explore models of “digital sovereignty” given the rapid diffusion of AI across society.

Why it’s likely

• Universities already train the majority of the world’s AI talent and have strong linkages to startups and global technology firms. Chinese institutions, for instance, produce nearly 50% of the top 20% of AI research talent.
  
• They host leading interdisciplinary research needed to shape AI governance, ethics and societal impacts, making them natural hubs for value-driven innovation.
  
• Institutions are responding to generative AI’s rapid uptake by students and staff, with growing recognition that ethical and responsible adoption must be defined and embedded across operations.
  
• Governments are seeking credible partners beyond the commercial sector — and could see public universities as anchors for human-centred, transparent AI ecosystems.
  
• Open-source development models centred on accessibility, transparency and multilingual capacity with strong university involvement are gaining traction — particularly in Europe (e.g. projects in Switzerland, France, Spain or Italy).

Strategic value

• Enables the creation of open, ethical and locally relevant AI ecosystems that reflect societal needs.
  
• Positions universities as anchors for AI research, talent pipelines and infrastructure strategy.
  
• Facilitates cross-institutional alignment, reducing fragmentation and producing shared artifacts — such as open model weights, toolchains, and benchmarks — that accelerate collective innovation.
  
• Offers a coordinated response to the ethical, educational and economic challenges of AI through public good-focused, interdisciplinary action.

Why it matters

This scenario is one where AI is governed and developed in the public interest. It leverages the trust, legitimacy, and cross-disciplinary strengths of universities to position AI as a civic and social project given its broad implications for citizens worldwide. In doing so, it helps ensure that AI benefits are more broadly distributed – and that equity, inclusion, human rights and democratic oversight are embedded into next-generation AI systems.

What we could build toward

This scenario positions universities as developers and stewards of AI aligned with public values. Universities already house the talent, trust and disciplinary depth to lead. This scenario thus isn’t about reinvention. It’s about aligning funding and coordinating action.

Universities could start by mapping their AI capacities — infrastructure, skills, governance and gaps — and then work together nationally and internationally to specialize, share and co-develop open-source value-driven artifacts. Public investments could prioritize these collaborations, tying funding to ethical governance, cross-sector engagement and open outputs. There’s also an opportunity for universities to embed AI literacy and ethics more deeply across curricula and to strengthen institutional levers — from procurement to data governance — that shape how AI is deployed in practice. Universities can and should help define what “AI for good” means and help make it real.