COMPLEX DIAGNOSTIC TESTS FOR ASSESSMENT OF INFORMATION LITERACY IN ARTIFICIAL INTELLIGENT RESPONSE SYSTEM
Information literacy is regarded as one of the key survival skills especially in digital environments. It can be regarded as a stay-alone battery of knowledge and skills allowing identification, selection, evaluation, and application of information, or as a part of digital competences (e.g. DigComp 2.1 framework). By the optimistic projections young people, tagged as “digital natives” (Prensky, 2001), are going to become information literates just by heavy use of digital, especially mobile, technologies allowing them to access information and to communicate anywhere and anytime. Later it was shown, that information literacy should be taught, e.g. by specially designed courses (Šorgo et al., 2017), because exposition to the technology alone is not sufficient.
Schools should be places, where adolescents get survival skills and capabilities not only to cope with recent challenges, but also to upgrade information literacy skills to technologies not known at the time of their schooling, media not yet invented, and new tricks of information providers. Therefore, authors of the Slovenian project (name blinded) are in a process to produce a free online artificial intelligent system (Authors, 2018) which will allow adolescents, and not only them, to achieve knowledge and skills as proposed by a framework and descriptors of seven domains of Information literacy (Authors, 2018). These domains correspondents with standards such as ACRL, DigComp 2.1.
To asses pre-existing and summative knowledge, multiple choice questions were assembled, following practices developed in previous project (name blinded) (Authors). However, the central role is given to the formative complex tasks, following best practices of PISA format of tasks, and two tier tests (e.g. Hickey et al., 2000; Treagust, 1988). Examples of tasks and test items, are going to be presented.
 ACRL. (2016). Framework for information literacy for higher education. Chicago: American Library Association. Online: http://www.ala.org/acrl/standards/ilframework (accessed: March 31st, 2018).
 Hickey, D. T., Wolfe, E. W., & Kindfield, A. C. H. (2000). Assessing learning in a technology supported genetics environment: Evidential and systemic validity issues. Educational Assessment, 6(3), 155–196.
 Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon, 9(5), 1-6.
 Šorgo, A., Bartol, T., Dolničar, D., and Boh Podgornik, B. (2017). Attributes of digital natives as predictors of information literacy in higher education. British journal of educational technology, 48(3), 749-767.
 Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students’ misconceptions in science. International journal of science education, 10(2), 159-169.