Higher education is always needed
Artificial intelligence in higher education creates great expectations for improved quality of teaching and learning. As a professor for educational theory and media education at the FernUniversität in Hagen, Claudia de Witt researches methods and applications of AI in studies, teaching and further education. Together with Niels Pinkwart (DFKI) and Florian Rampelt (Stifterverband) she published the whitepaper “Artificial Intelligence in Higher Education”. In an interview with the KI campus, Prof. de Witt describes the disruptions to be expected in higher education from an educational science perspective and what role AI can play in this.
Ms. de Witt, what fascinates you about the research field “AI in Education”?
For many years I have dealt with the importance of digital media for educational processes, with the digitization of teaching and learning and also with human-computer interaction from an educational science and media pedagogical perspective. What fascinates me about it is the innovative potential associated with technologies and artificial intelligence, and with which we can shape the future of learning and education as we imagine it. At the FernUniversität in Hagen, I have found an excellent environment for this, in which I can combine educational theory requirements with educational technology solutions and contribute to research and development for a contemporary digitalized higher education. What particularly fascinates me about artificial intelligence is that this technology has the potential to provide a kind of personal assistance - in learning and teaching - and can help us to better understand our cognitive and metacognitive abilities - our thinking, our problem-solving and our decision-making.
AI can help address various challenges in higher education. Where do you see the greatest potential?
Since the Dartmouth Conference in 1956, artificial intelligence has been a scientific subject that deals with machines, robots and software systems that perform complex tasks independently, for which human intelligence is a prerequisite. And I believe that these skills can be used in higher education. Knowledge-based systems can be used in combination with machine learning methods to promote both the acquisition of knowledge and the development of students' skills. Artificial intelligence can evaluate open questions in real time and is the basis for intelligent automated assessment; The individual contributions by students can also be assessed with an automated evaluation, which is referred to as "Automated Essay Scoring". Chatbots answer administrative questions from employees and students during ongoing operations. Educational data mining models recognize the learning progress, motivation and metacognitive states of learners over a longer period of time and enable automatic reactions in the form of AI mood analyzes or prediction systems. With AI, for example, courses and modules could be set up according to the requirements of personalized learning and personalized competence development. The teachers can also be accompanied by a kind of “AI teaching assistant” who helps them improve the quality of their teaching.
Adaptive, personalized learning formats that specifically support the diversity of students with their strengths and weaknesses as well as their self-regulation and self-efficacy in their studies and accordingly adapt flexibly and "intelligently" to their individual learning needs are contemporary. AI-supported learning platforms and intelligent assistance systems in the form of recommenders or (language) bots can record the specific needs of the students, focus on their learning goals, strategies, organization and progress and with suggestions the further learning process in all study phases individually support. In addition to knowledge-based expert systems, machine learning and learning analytics, academic analytics and educational data mining are used. Hybrid AI systems are useful because, for example, they combine the advantages of machine learning with domain modeling and expert knowledge and contain principles such as explainability, predictability and traceability.
The basis for the successful use of AI systems in higher education is reliable data. How and where is data generated at universities and in what quality is it available?
Data at universities are created at the latest when you enroll. This is followed by initial surveys, satisfaction surveys, evaluations of events and course materials, and exam dates are available. With the so-called ECTS monitoring, study activities and progress can be measured by determining actual and target ECTS points and the course of studies can be traced. In the learning environments, the activities of teachers and learners also generate data. For AI applications, this data must be pseudonymized or anonymized in the interests of data protection. The quality of the data for an intelligent system is then particularly dependent on the linking of the data from the different sources. Basically, however, you always have to critically question how the data was created and in which situations it is used again.
What kind of information can a digital footprint provide about student behavior?
Students generate data through their logins into the learning environment of the university, through their activities in forums, blogs, quizzes, and through their reading and writing activities. These data provide information about their learning behavior, learning strategies, media preferences or communication behavior, for example. For a close coordination of your individual learning goals, content, speed and results in interaction with the system, a filtered selection of learning content can be used, for example. In addition, good feedback systems such. B. for supporting self-assessment and self-efficacy (“Think about using a different learning strategy”). Checklists are no longer sufficient for personalization, for this we need adaptive (feedback) systems that are able to record the needs of the students and give individual recommendations for the further learning process.
This includes, for example, images and visualizations of learning progress; With the help of data, learning needs can be made visible, reports can be created for students and used for their own assessment of the activity levels achieved (“How do you want to be measured?”). After a while, adjustments can be made (“Your expectations are too high”). And “translanguaging” (automatic translation) helps students to assess their language skills, to understand and express their knowledge in their own terms.
I think our current LMS will change in this direction in the medium term. This requires a data-oriented understanding from the learning individual. Information about who the learner is, what his / her competencies are, what he / she is doing and what his / her goals are, are just some of the information that is needed to design a tailor-made support. In addition, the approach not only requires an orchestration of technical systems and technologies such as machine learning, learning analytics, recommender systems, etc., but also the involvement of learners and teachers who have yet to learn how to use learning analytics dashboards, recommendation systems and personalizing adaptations in order to generate an optimal benefit for yourself.
In principle, only such information should be collected and used that is important for the students. And when student data is evaluated and used, this should be made transparent and understandable, i.e. what data the AI system processes and why certain results are obtained. This knowledge in turn is the basis for students to be able to act independently and independently. We have to conscientiously develop Artificial Intelligence and cultivate an ethically justified approach to AI at the university. For example, clear specifications and evaluation procedures help. AI systems should therefore be explainable AI systems with comprehensible outputs and decisions.
AI can support teachers by providing better insight into student needs. Can AI replace teaching staff completely in the future?
I don't think AI will completely replace teaching staff. The technology will certainly relieve the teachers of some activities, for example routine corrections of exams or constantly recurring questions. AI systems are able to access an infinite number of databases worldwide in a short time, to suggest relevant sources and current studies to the students for their individual study achievements more quickly, and even to process them if necessary. The personal contact with the teacher, who knows more than what is on the Internet, the personal support, their critical thinking and their experiences are, in my opinion, irreplaceable. Knowledge requires judgment and only the evaluation, classification and interpretation of data can constitute knowledge. And a good teacher is always better at this than a machine. In addition, factors such as the empathy of a teacher remain essential for the motivation of students.
On the other hand, teacher-oriented AI applications can help teachers improve their own teaching. In order to use these developments in teaching, new concepts in university didactics are required. In these concepts, on the one hand, the technological, personalized support of the students must flow and, on the other hand, more space must be given to the task of the teachers as promoters of critically thinking, self-determined and socially capable individuals; I am thinking, for example, of a kind of hybrid didactics that takes into account that interaction with technology is becoming more and more natural and communication takes place via our language and gestures.
How do you assess the developments: Will teachers, employees in support structures and administration as well as university management and students be able to deal competently with AI systems in the future?
From my point of view, there is still a long way to go before we are this far. University management, teachers and students need to know that we are moving towards a strong "mathematization" of higher education. AI in higher education means facing datafication of teaching, study, and research that includes predictability, predictability, and control operations. All actors in higher education have to deal with the associated disruptions and be open to the possibilities or be aware of the limits of AI in higher education. For this, competencies about, with and despite AI are necessary, as we have described in the white paper "AI in higher education".
How could the expansion of competencies in higher education be promoted?
The online course "Elements of AI" and of course the KI campus with its open online courses for acquiring competencies about AI are a good example of expanding skills through digital educational offers. It also seems to me that new courses of study and also smaller educational offers on AI are emerging at many universities. Because there is now more and more the insight that you have to make yourself smart about artificial intelligence, big data, machine learning, etc., because we are supported by AI not only in the university, but also in the professional context and above all in our everyday life technology is “getting closer and closer to us” and we are talking more often about “augmented intelligence” and “augmented learning”.
At universities, for example, students could get to know the possibilities of AI through research-based learning (“inquiry learning”), practice on practical challenges and develop innovative solutions, i.e. get to know the basics and tools, in order to then implement their own AI projects in practical phases. Competence development should also be integrated into the curricula of the undergraduate courses. This aspect should play a role in future accreditations. At the moment there is already a lot of talk about the need to impart digital skills, but this has not yet been implemented to the necessary extent and speed.
The interview was conducted by Lavinia Ionica, program manager at the Hochschulforum Digitisierung.
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