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EMPOWER webinar week on Artificial Intelligence

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Our next EMPOWER webinar week is coming up, dedicated to Artificial Intelligence in Online Education.

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EADTU is organising the Artificial Intelligence in Online Education webinar week from 16-18 June 2020.

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Over the past decade, AI has increasingly captured attention in the educational field. This growth has been highlighted in several reports (Baker, Smith, & Nandra, 2019; EDUCAUSE, 2018, 2019) and in the increased number of academic publications as well (Zawacki-Richter et al., 2019). AI in education (AIEd) practice can be clustered in four areas: (1) adaptive system and personalization; (2) assessment and evaluation; (3) profiling and prediction and (4) intelligent tutoring systems (Zawacki-Richter et al., 2019). AI in education can serve needs of target groups consisting of educators, educational leaders, support staff and learners. More concretely, artificial intelligence can benefit HEIs dealing with: (1) teachers workload, they are overwhelmed with the amount of works they have to deal with; (2) lack of personalization in the offerings, the principle followed is “one size fits all”; (3) narrow assessment, based on text and exams; (4) difficulties of sharing insights between HEIs; and (5) inequalities in the access to education (Baker et al., 2019).

In a series of webinars related to AI in education, EADTU wants to address these topics and related ethical issues with experts in the field. Please join us online and explore together the potential of AI in education.

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Programme:

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Tuesday 16 June 2020 [14:00 - 15:00 CEST]:

Looking deeper Artificial Intelligence in Online Education

  • Artificial Intelligence in Teaching (AIT): A roadmap for future developments (Wayne Holmes, Nesta, The United Kingdom, José Bidarra, Universidade Aberta, Portugal, Henrik Køhler Simonsen, SmartLearning, Denmark)

The AIT project started in 2019 and aims to identify and analyse AI best practices in HE in three countries to develop a road map for future developments and use of AI. The AIT project will investigate three dimensions of AI in education: learning ‘for’ AI, learning ‘about’ AI, learning ‘with’ AI. The focus will be on identifying examples and best practices of AI in HE (across all AI dimensions). The analyses will include outlining national characteristics, specific technologies, and didactic and pedagogical approaches to AI in HE in the United Kingdom, Portugal and Denmark.

Simulating a real biology laboratory can enhance student training and better prepare students for the actual on-site experience. At the same time, the increased amount of practice can improve safety, and reduce equipment wear and tear, as well as reduce the usage of consumables. A production-level system, Onlabs (http://onlabs.eap.gr), now also supports activity scoring but, for this to scale up to the number of devices used and experiments carried out in a real laboratory, a semi-automatic approach is being developed to facilitate the elicitation of expert knowledge as to what constitutes a successful experiment. We review the R&D set-up and present machine-learning based techniques for capturing the scoring patterns of expert instructors.

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Wednesday 17 June 2020 [14:00 - 15:00 CEST]:

Good practices and ethical issues in Artificial Intelligence in Online education

  • Empowering education by artificial intelligence (Jesus Boticario, UNED, Spain)

There is nothing new in realising that Artificial Intelligence (AI) is used in educational processes (AIED) with an increasing level of success for over four decades of research and development (Lane, McCalla, Looi, & Bull, 2016). What is new is that it is a felt demand to use it already as a key tool useful at all levels of education based on the intelligent management of a growing number of data and resources. However, AIED at scale entails organisational, educational and learning changes in all educational levels. Higher Education Institutions (HE) have a head start on this and an urgent demand to fulfil because both they have more technological infrastructure in place and have been collecting data for long and students are already used to enjoying personalisation in their daily activities "barring" learning. But first things first. Do we have a clear idea of what is personalised learning and how the whole educational system has to be refocused so that each learner becomes the main centre of the learning process? This needs for a global approach that covers all that is involved, starting from the nature of the daily tasks of the main protagonists, teachers and “learners”. It also considers the need to properly take care of large-scale data-sets which account for authenticity, consistency and transparency, careful management of data learning processes, privacy and ethical issues. All this along with organisational changes are arguably achievable goals if the approach is based on: 1) Clear analysis of the implications in deploying personalised learning at scale, 2) Supervision and assurance of governance in terms of the intertwining relationships among AI, data and ethical issues involved, 3) Massive production of digital materials which are to comfort to standards and interoperability requirements, 4) Methodological and technical support to implement the required infrastructure and 5) An unshakeable commitment with taking on board all the stakeholders involved before making the required profound changes to make this happens, i.e., changing from “education” to “learning”.

Personalized learning environments have become a highly preferred structure with the developments in information and communication technologies. In particular, the use of artificial intelligence (AI) tools is one of the most preferred modern approaches in the provision of personalized learning. These AI tools are capable of being utilized for different use cases, such as learning preferences, assessment results, learning outcomes, or communication preferences, depending on students' attitudes in educational environments.

LIS project has been initiated at the Universitat Oberta de Catalunya (UOC) within the scope of intelligent learning systems to accomplish the mentioned rationale. The main objective of the LIS project is to develop an adaptive system to be globally applicable at the UOC campus to help students to succeed in their learning process. It mainly has predictive analytics and recommendations designed upon artificial intelligence techniques. Predictive analytics tries to predict the students' behavior individually based on historical data and current activities to prvide personalized recommendations. Also, LIS gives support to teachers in the daily work to provide direct support and feedback to help students passing the courses.

The presentation will focus to present three main results of the project until now: 1) the predictive models used to predict students’ risk level of failing the course; 1) the developed infrastructure to support the complete system; and 3) the features for students and teachers in order to provide support during the learning/teaching process.

We consider that this webinar could be of interested for the audience since it will show a fully functional system based on AI techniques for helping on education.

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Thursday 18 June 2020 [14:00 - 15:00 CEST]:

Good practices and ehtical issues in Artificial Intelligence in Online education

  • AI applications in higher education - challenges and opportunities in ODE(Prof. Olaf Zawacki-Richter, Ph.D., Carl von Ossietzky University of Oldenburg, Germany)

Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for many educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. Based on a systematic review of 146 studies, Olaf Zawacki-Richter will provide an overview of research on AI applications in higher education in four areas: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. A stunning result of the review is the almost lack of critical reflection of risks and ethical issues of AIEd. Challenges and opportunities of AIEd will be considered for the field of open and distance education (ODE).

  • I would not worry so much about buying an intelligent learning system but spending money on buying a sexist one (Covadonga Rodrigo, UNED - Spain & Francisco Iniesto, OUUK - United Kingdom)

The data refute that today most software developers are male and only 27.5% of developers in the world are women. Already in 2015, Amazon realized that their recruitment system did not judge in a gender neutral manner. It had a bias in favor of men when examining candidates for software developer positions and other technical occupations. The problem seemed to stem from the fact that the machine learning specialists had trained the artificial intelligence tool from patterns that could be observed in the curricula presented to the company for a decade, and most of them belonged to men. The result of male domination in programming has led to the development of, for example, voice recognition technologies that, trained and tested only by men, struggle to understand female voices. The industry is already filled with services and products that have gender bias effectively programmed into them.

Even though modifications can be made to the software, it is hardly difficult to assure the absence of biases. As artificial intelligence becomes an increasing part of our daily lives, educational institutions from all academic levels are being transformed by intelligent systems that might help humans learn better and achieve their learning objectives. AI systems can be used to tailor and personalize learning for each individual student, developing a custom learning profile of each student and customize the training materials for each student based on their ability, preferred mode of learning, and experience. This can bring a high impact on the learning of disadvantaged groups, such as students with disabilities or people in risk of inclusion. But precisely, these groups need the systems did not devise other forms of discrimination.

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