Artificial intelligence plays an important role when higher education institutions build guidance and counselling services to support studies in the Digivisio 2030 programme. Jyri Kivinen from Lapland University of Applied Sciences and Olli Hotakainen from Tampere University have, as a partial implementation work, examined the challenges, opportunities and best practices that can be identified in AI-based guidance. The observations were compiled during autumn 2022 in workshops to which participants from different higher education institutions were invited.
Based on the study, many higher education institutions are carrying out a project or experiment that includes student guidance supported by artificial intelligence. Systems that utilise learning analytics and artificial intelligence are also under development, but they are not based on highly advanced AI. Simple chatbots are most commonly used. More advanced bots can seek out students in need of support (Anniebot) or recommend studies or jobs (3AMK’s CareerBot).
Problems related to artificial intelligence can be solved through preliminary planning
Based on the study, a major challenge in guidance supported by artificial intelligence is that it is not yet used a lot and it is a new tool for many employees. Artificial intelligence systems can also be expensive and introducing them involves a lot of ethical questions and information security challenges. The most significant challenges identified in the workshops were the lack of resources in the development work, utilisation of artificial intelligence only in stereotypical cases, insufficient availability of data and difficulty of automating pedagogical practices.
These problems could be solved through careful preliminary planning. Staff in higher education institutions could be provided with artificial intelligence training on, for example, the collection, use and protection of data. Additional resources should be allocated to development work. In addition, a uniform system in which data and its utilisation comply with the same standards and rules should be created for higher education institutions. The Digivisio 2030 programme could play an important role in this work.
Artificial intelligence could help in studies, assessment and career path planning
So, where could artificial intelligence be utilised? According to the study, artificial intelligence could support learners in applying for studies and planning them. It could recommend training programmes and build study paths based on, for example, the person’s work history and interests. In order for artificial intelligence to perform this task, competence should be described in the same terms in both education and work. It should also be remembered that artificial intelligence cannot replace discussions with an actual study or career counsellor. The learner must also have the power to decide how their data is used.
During the studies, artificial intelligence could help the learner in, for example, time management. In the future, each learner could have their own artificial intelligence mentor. However, it should be noted that the learner must be able to decide on the use of their data and that the learner must see how their data is used. Excessive reliance on artificial intelligence may also affect self-direction.
Teachers could be supported by artificial intelligence in, for example, the assessment of micro-learning and micro-credentials. Artificial intelligence could facilitate the assessment process and, as it develops, diversify automatic feedback.
Recommendations for Digivisio and higher education institutions
The preliminary study recommends that higher education institutions begin building the conditions for collecting study data even before the introduction of artificial intelligence systems. Higher education institutions must also invest in strong preliminary planning in information security and ethics before introducing AI systems.
According to the preliminary study, Digivisio could play a major role in creating common rules concerning data so that national terminologies and guidelines could be introduced. This would later enable the building of cross-institutional systems. Common rules for the production and collection of data and common guidelines for terminology and ethical practices of study data could be prepared utilising the competence of experts in Digivisio’s Data theme group.
Read a summary of the preliminary study: download the material.
Read the full report (in Finnish): download the material.
More info
Sini Hakala, E-learning coordinator
sini.hakala@csc.fi