The Commodization of the University IT: does IT matter?

We usually think of a university as a geographic place, with buildings on a campus where knowledge is created, shared, and taught. Such a place needs infrastructure: computer networks, WLAN access points, and servers. Really? Whether researchers need to work in a geographic location is likely to be questioned quite a bit, at the latest after the Covid pandemic. Individual disciplines, especially in the humanities and social sciences, almost wholly retreated to the home office, and for many universities, it takes work to bring them back to campus for face-to-face teaching.

So is returning to the University necessary for research disciplines that don't use equipment or labs? Well, yes, shout the students! Ahem, no, say those researchers who had positive experiences lecturing over Zoom, sharing materials with Moodle, and organizing stuff using Teams and OneDrive. If the students can not convince them to return, is there an argument for conducting research on-premise?

Not likely. Scientists are tasked with gaining and disseminating new knowledge through research and publications. Most publications are co-authored with other researchers, preferably at another university. Thus, authors co-edit the research work on commonly available platforms: M365, Google Docs, and Github. Why? Because it does not require to access proprietary infrastructure in another university, usually requiring cumbersome remote access procedures, VPNs, and temporary guest logins. It is just easier to use commonly available, commercial cloud-based systems.

Does it make sense for a university to provide digital resources to individual researchers? The university app, where the canteen plan is the most used feature; the costly lab management software that only two research groups use; the ample data space on university servers, where nobody ever deletes obsolete files? Depending on the scientist and the discipline, providing exclusive hardware and software can be very complex and costly, and the "return on investment" (ROI) for the particular university is questionable.

University IT is on a clear path from being a unique, competitive resource to a commodity infrastructure. Nicholas G. Carr started this discussion for other sectors, but it took some time to reach higher education. Ultimately, digital transformation may thus result in a maximum internet connection on campus so scientists can share their data and texts using digital infrastructure from a commercial cloud or a supercomputing center. Of course, the university would have to pay annual fees for access to the cloud or the data center; but they would save investment in buildings,  energy resources, compute servers, and data servers. The new problems that this creates, are subject to another blog post.

Three C's and their vitamin shock for Universities

The importance of the last five years for the digitization of Universities cannot be overestimated. More has happened fundamentally in these five years than in the 30 years before, and the reason for this lies in the three "Cs": Cloud, Covid and ChatGPT. Together, these words represent 3 vitamin shocks and will massively change the way universities approach their digitization.

The first "C" is the cloud. As I've written elsewhere, the availability of Amazon Web Services, Microsoft 365 or Google for Education is a fundamental way to get rid of your own data center. However, this is at the cost of a Babylonian captivity to the cloud provider, who, exploiting the lock-in effect, can drive a pricing policy that brings universities to the edge of their own funding possibilities as the level of digitization increases. But in the best of all worlds, there is always the latest software, worldwide accessibility of the platform and a 24/7 helpdesk.

The second "C" in 2020 was the shock of the Covid19 epidemic. At breakneck speed, universities flexibilized face-to-face teaching, introduced flipped classroom concepts, and enabled distance exams. Instructors recorded thousands of instructional videos, which were so well received that even after the pandemic ended, students do not want to give up hybrid teaching and the options they had once learned about. Back to large and crowded lecture halls with solo entertainers at the chalkboard? Not with our students! Similarly, the impact on the work modes of administration and academics. Home office for administrative staff: an absolute rarity in 2019, quite common since 2021. Work meetings, even scientific conferences take place via Zoom.

The third "C" in 2023 is the worldwide availability of generative artificial intelligence (example ChatGPT), whether for the creation of texts, images, or software. In addition to the de facto death blow for the essay assignments, generative AI also enables a leap in productivity, be it through easier writing of scientific texts, simplified summarizing of sources, better translations or better graphics. When Richard Socher was asked at DLD23 about GAI's current failures (hallucinations) in properly linking sources, he succinctly said, "Give us a few more weeks" (addendum: LLaMa, GPT4).

For me, the essential commonality of all three vitamin C shocks is that they happened past the traditional university data center. The old servers are no longer needed. What we do need, however, is a university-wide digitization strategy, knowledgeable staff for local support, and a training campaign for using the new services. Are our universities properly positioned for this in terms of personnel and funding?

Translated with DeepL

Does ChatGPT level the differences between the world's best universities and everyone else?

Is there a power imbalance between American and European universities when it comes to publication impact and research funding? Of course: elite institutions like Harvard, Stanford, and Oxford lead the way, while smaller universities such as Bayreuth linger in the long tail. This holds for the attractiveness for younger researchers searching for a career, the impact factor of publications, and usually the endowment with research funding.

Will AI, available to everyone, even out the playing field? Is it a game-changer? It might be since tools like ChatGPT and other AI solutions are accessible globally. All that's needed is an internet connection and a desktop computer. It won't matter where the researcher is located--whether it's Mombasa or Miami, Bangalore or Boston, Frankfurt or San Francisco--as long as they know how to make use of generative AI and publicly available data.

The conversations about the implications of AI on universities can be looked at in two ways. Supporters of the concept posit that high-end universities have the capacity to use their wealth and receptiveness to technology to carry out AI research projects, resulting in a greater disparity in resources between these universities and those without as much financial backing.

On the other hand, one of the greatest opportunities afforded by AI is the democratization of opportunity. Through the use of open-source software and resources, anyone from anywhere can access the same tools and data sets. This evens the playing field for everyone, regardless of location or means. Furthermore, success in using AI will depend more on creativity and innovation than financial resources, allowing those with a limited budget to still make advances in their research. In this case, the chasm could decrease as anyone with access to open AI resources has the potential to succeed, regardless of their geographic location.

Attending DLD23 in Munich in January 2023, I was lucky enough to approach Erik Brynjolfsson and Andrew McAfee with this question. Their answers differ in detail, but the general sentiment is: the power curve is rock solid, and if you are a small university somewhere in the long tail, you have to act fast.

Erik's answer was: The power law between American and European universities will likely still be in effect when it comes to publication impact and research funding. Elite institutions like Harvard, Stanford, and Oxford will continue to lead the way, while smaller universities such as Bayreuth will remain in the long tail. However, AI could give a new competitive edge to universities that are quick to act on embracing the technology. Those that provide their researchers with technical access and capabilities, use AI in teaching and exams proactively, and create an AI-friendly environment that allows for experimentation with data sets have the potential to move up the power law curve faster than those who are slower at adapting. On the other hand, those who try to suppress using AI or go back to traditional methodologies risk further falling behind. Ultimately it is up to each university’s leader(s) to determine whether they want to move up or slide down this curve.

Andrew's answer: We do not know the answer to that question yet. However, most new technologies have throughout the years increased the effect of the power curve, one way or the other. What might happen if you look at a particular researcher, is that AI is a diamond seeker, meaning that AI is very much capable by sifting through tons of research writings to detect promising new research better than humans might do (e.g through a manual structured literature review, which is always biased). In that case, researchers will get increased visibility even if having few publications (early career) and then be capable to move to a better university up the power law curve. Even when you have the same access to research tools regardless of geography, it is still more attractive to the particular researcher to move where brains who you can go to lunch with, are in one place.

Disclosure: this blog post has been drafted, translated, edited and proofread using sudowrite.com and deepl.com.

The future of exams according to ChatGPT

 Artificial Intelligence (AI) is the current buzzword of digitization, and generative AI for creating texts (e.g. ChatGPT), images (e.g. DALL-E) produces the most memorable results for a wide audience. As is always the case with software innovation, the longer-standing development of middleware (OpenAI) with its data science algorithms has not received the same media response, simply because it is harder to understand and image. With generative AI, it's easier, and that starts the hype curve -- from inflated expectations to disappointed illusions.

For education, the capabilities of generative AI, especially in text creation, represent both risk and opportunity. The range of AI support in this regard is broad. Arguably, no one would object to assistance in creating an outline, an abstract, help with translation into English, or making sure a text is grammatically correct. We have problems with the fact that in the preparation of an examination performance (especially as a text: exam, term paper) the performance of the AI is so comprehensive that it eclipses the individual performance of the human examinee. We would like to measure and evaluate the individual performance of the examinee!

As long as we measure the performance of the human in a category that can also be done by the machine, we have only ourselves to blame. There is a reason that after the advent of the pocket calculator we no longer ask for mental arithmetic in school math exams. Both in the teaching of knowledge and in the measurement of performance, we have to be realistic about the fact that environmental conditions have changed and a new technology is available for the long term.

To make it as realistic and practical as possible, we prepare students in an exam for a real situation of a task solution under time pressure:

1980er: "Meier, please come quickly to the boss and explain why the booking was done this way. You have learned that, haven't you?". The logical form of examination for this is a "closed book" exam, in which you have to reproduce something you have learned by heart without any aids and with only paper and pencil. Why? Precisely because the practice later asks exactly the same questions as we ask in the exam.

2000er: "Meier, in two hours the presentation will be at the board and we need a quick compilation with the proposal and the reasoning. Fire up the Internet and find some sources." This is, of course, an "open book" retreat, where all the tools are available, but the solution must be aggregated, concisely summarized, and presented from a variety of relevant and irrelevant information. (If, beyond this compilation and with a little more time, all available information is sifted and an own idea is to be developed on this basis, we are at the examination performance of the written term paper or thesis. This is already "open book" anyway).

We can presumably assume that from 2023 onwards, it will no longer be just "the Internet that is switched on", but Mr. or Mrs. Meier will use a generative, text-creating AI such as ChatGPT to solve problems in the business reality of the future. Consequently, we will also have to adapt our university examinations accordingly. In any case, prohibiting AI will not lead us into the future.

Translated with DeepL

University professors manage a curated playlist

 The availability of all kinds of information on the Internet brings us to a new role for professors in university teaching. They become trusted intermediaries for knowledge acquisition.

As a professor, when I prepare new teaching materials for lectures, I naturally search the Internet for appropriate graphics, case studies, videos and other information. It is always amazing what material can be found there -- it ranges from incredibly well-prepared materials (e.g., Hans Rosling's TED Talks, which can be adopted almost unedited and in full beauty) to politically one-sided, colorized, or just plain wrong information. Using my knowledge and experience, I pick out the sound and credible media from this haystack and present it to my students in lecture.

The more external material I incorporate in this way, the more I create a playlist. In an earlier post, I did use the music analogy, and here it is similar. My students have a kind of basic trust in me as a lecturer, that the "music" I play will help them in their acquisition of knowledge.

*In the past* the lecturer was a "single source of truth", a "gatekeeper", who transferred exclusive information from the world of science into the teaching world. In the past, the only music I could hear was the music I played myself. In addition to this source, there was the library, also curated by a gatekeeper*, with further reading.

This world has changed. Similar to the transition from edited encyclopedias to unedited Wikipedia, the role of the former gatekeepers must change with it. We professors must drown out a growing background noise of dangerous half-knowledge as we attempt to share our knowledge. From all sides and even during lectures, our students are bombarded with unfiltered music, texts, and images on the very topics we want to teach, meticulously prepared and neatly supported with citations.

What do I have to do? Put my beautiful music against the ever louder noise and become louder myself, more pointed, more opinionated, more pointed, so that I can be heard? So that perhaps less balanced and less scientific? That can't be it.

Rather, I now see my task as compiling a playlist for students from the available music (= information from around the world). This playlist is curated, it can be followed, it can be liked, it brings my students closer to a topic -- in a natural process that is comprehensible to them and that is obviously gaining a foothold in other fields.

We just have to overcome the not invented here syndrome. That exists with professors, too, but that will be another post.

Translated with DeepL

German Universities and the cloud -- what do we need to do?

The accelerated digitization of German colleges and universities in 2020/2021, in response to the COVID19 pandemic, would not have been possible without the massive uptake of cloud offerings. Teaching via video conferencing systems such as Zoom, digital remote exams with and without proctoring, and collaborative work via collaboration systems such as Confluence or Microsoft Teams found their way into the normal working day virtually overnight. More digitization happened in a few weeks than in the years before. Cloud providers such as Zoom, Google, and Microsoft captured shares of the digitization budget to a much greater extent than ever before planned. It became very clear during this period that the pre-pandemic digital world was caught up with a reality for which the universities and their administrations, which were nevertheless quite slow to act, were not prepared in their basic structures.

Cloud services extend the IT services previously used in universities and are mostly provided by their own data centers. They can be set up quickly and decentrally on PCs and notebooks and used by university staff. They do not lead to the acquisition of new large-scale equipment in the data centers, but they do lead to demand for user support from existing IT staff.

Utilization of these additional options, which are intended to improve the attractiveness and competitiveness of a university, is only possible after fundamental, internal considerations. Whereas in the past a data center or IT department could decide for itself whether to introduce software, the use of cloud services raises strategic issues that can only be resolved at the level of university management. The relocation of parts of the IT infrastructure, including data storage "off-campus", has implications for digital sovereignty, the design of basic infrastructures, or personnel development. In addition to new opportunities, unexpected dependencies arise. Recommendations for the concrete use of cloud services in universities were published by ZKI e. V. in 2021 in a result report with specific recommendations. The HRK has taken up the topic with Circular No. 26/2021.

There are strategic questions that must be fundamentally answered at the level of university managements, CIOs, ministries, federal states HRK before cloud use. They concern organizational, legal, and financial framework conditions.

Digital sovereignty: Most German universities operate their own data center. There are many reasons for this. Cloud services have been added during the pandemic. With cloud services, there is perceived uncertainty about the long-term development of license fees, as well as a dependency due to the increasing integration of cloud services into everyday operations (vendor lock-in). In the future, universities will have to weigh up on a political level whether such uncertainties and dependencies should be accepted or whether providing services in their own data center is fundamentally a sovereign path.

Cooperation structures: Private clouds could represent a middle ground between a company's own data center and cloud services from commercial providers. These could be either inter-university offerings via state data centers or the direct provision of services between universities. The decision on cooperation will have to be made partly by the universities themselves and partly in dialog with the state ministries. This also raises the question of the institutional structure of the cooperation. The state of Bavaria is attempting to establish this cooperation as a statutory task by founding a digital alliance (cf. Art 6(5) of the Higher Education Innovation Act) and to address the issues jointly across all types of higher education institutions.

Fiscal aspects of cooperation: the purchase of IT services is subject to VAT unless it is scientific cooperation. This applies to commercial cloud offerings (Microsoft 365, Zoom), but also, depending on the design, to inter-university offerings via the above-mentioned cooperation structures. Unless there is a provision in tax law here, e.g. that cooperation between universities is a public task, an IT service via cooperation may be up to 19% more expensive than providing the same service via an own university computer center.

Procurement: the current ways of procuring IT services are geared toward operating their own university computer centers. In the nationwide DFG funding program "Grossgeräte der Länder", they are half-subsidized with federal funds. Therefore, state-funded universities have no incentive to consider cloud services as a substitute if they have to pay for them in full.

Support structures: academics are used to being able to request assistance from commercial data centers, networks, and cloud operators 24 hours a day, 7 days a week. Small to mid-sized universities cannot provide this support. What does this mean for future workforce planning?

Sustainability: Increasingly, the university's own IT provision needs to be climate-proofed. For the state of Bavaria, an initial study has revealed a need for construction measures in the double-digit millions to meet the climate targets set by the state and federal governments concerning existing university data centers. Should these investments still be made when "everything is going to the cloud"?

The search for legal frameworks regarding the necessary cloud use in a European, or better, the inner-German legal framework is obvious. The Zoom provision of the DFN via the Telekom cloud shows an inner-German way for personal data of students and researchers, the NFDI for research data. However, cloud-based delivery of online university elections is not yet possible in many countries and is the subject of ongoing legal discussions.

With these issues in mind, it is necessary to take a hybrid view of the reliable and sustainable provision of IT services and to create prerequisites in each direction. The goal must be to efficiently operate IT infrastructures for universities that have been built and maintained with tax funds.


Translated with DeepL, edited with Grammarly.

Shadow-IT and the University: a mixed relationship

This post (in German language) by my colleague Prof. Steffi Haag beautifully illustrates the opportunities that the use of shadow IT can also have in the university.

I often think of shadow IT as reinventing the wheel: chairs running their own Exchange servers, NAS boxes stored under the desk in the secretary's office, meeting rooms converted into PC pools. I don't raise my eyebrows at the expenditure of material resources; each professorship should know for itself what it spends its budget on. The difficult part is usually the human decisions behind it. First of all, investing in shadow IT requires a professor who is convinced that the central IT services of the university do not meet her demands for IT services. Steffi Haag writes about this mindset:

However, our research of more than 85 shadow IT cases shows that the use of shadow IT is often rooted in such restrictive corporate practices themselves. Specifically, when employees face an impasse that prevents them from achieving their goals. Whether it's because they fall on deaf ears in their company when it comes to new digital solutions; whether it's because the use of new digital technologies is limited, such as tablets whose use is restricted to certain apps; or whether it's because companies cannot fully meet the needs of their employees, for example, due to legal requirements such as the GDPR. Whatever creates the sensation of an impasse, shadow IT offers employees a way out.

This kind of sensibility seems to be even more widely represented in the university than elsewhere. This is in the nature of things: precisely the activity in research has, by definition, to do with the search for a way out of a dead end! No wonder that professors also apply this to their IT problems. Many interesting IT solutions have been brought to my attention by individual chairs trying them out -- be it edTech tools or electronic lab books. I am happy to discuss this with professors in the designated presidential or senate committee, have these tools demonstrated to us, and many a time also make procurement decisions.

Sensitivity, however, all too often turns into incomprehension when the commission does not want to follow the idea and the proposer does not find this reasonable. The above list of reasons why a company (or a university) does not use a great new solution does not mention one central point: the lack of budget and personnel. Every Euro we spend on IT does not go directly into research (staff positions) or teaching. Every Euro spent on shadow IT takes away one Euro from publication fees, travel, and human resources. Every staff hour spent on Exchange server maintenance takes a PhD student further away from her dissertation.

Despite all the opportunities, for reasons of efficiency, principally I have to take a side against shadow IT in the university.

Translated with DeepL

Leave the digital transformation of teaching behind you!

 As I wrote in an earlier post, shortening "digital transformation of the university" to "digital transformation of teaching" seems to me to fall short. And for that, you don't have to resort to the Humboldtian ideal, but simply look at what members of a university spend most of their time doing -- at many universities, it's research.

Sure, professors go into the lecture hall at least once a day (in pre-Corona times), staff members prepare slides and supervise seminar papers, and the secretary's office sells lecture notes and accepts applications for recognition of exchange. These activities do not change, from semester to semester, from day to day a similar procedure. Hundreds of such operations in a month in a department, thousands in a faculty, hundreds of thousands in a university. So you might get the idea that these (repetitive) activities are the reason the university exists -- and you couldn't be more wrong.

Because researchers are actually driven by the search for the creative, the innovative, the insight. The next discovery, the lab result, the scientific discussion, the publication is the means to get ahead.

If we see digital transformation only as a means of doing what we do more efficiently: if we supplement our own face-to-face lectures with digital videos, store research data in a national cloud instead of on our University servers, offer MBA courses globally in English -- where is the strategy there? It's a digital extension of the same, a step into an international arena whose rules of the game we need to understand before we can make sense of it.

Digital transformation is changing a lot of things -- including university strategy. Universities with excellent, high-profile basic research can monetize education and training in these areas. Universities with impressive campuses create a sense of belonging that inspires alumni decades later. Much research and teaching content transports itself over the Internet -- depending on whether a university transports itself as a sender or receiver of this content, its own strategy must adapt.

You can spend a lot of money on digital transformation of universities; but it should be spent strategically in the right place. I doubt that this money is well spent on digital teaching.

Translated with DeepL