---
res:
  bibo_abstract:
  - In today's technology-driven world, the need for interdisciplinary skills is increasing.
    This has become challenging in tertiary education to provide students with applicable
    knowledge of various fields. Anderson's Adaptive Control of Thought (ACT) theory
    suggests that universities have traditionally focused on imparting declarative
    knowledge, which involves memorization of facts and concepts. However, imparting
    the ability to apply such knowledge on individual students and create procedural
    knowledge is the challenge. This includes teachers dealing with a diverse range
    of student abilities, particularly at university-level where they teach the same
    course content to students with different levels of prior knowledge and, given
    the structure of modern education systems, the resources required to monitor and
    provide feedback for a number of decisions and attempts independently performed
    by the students. Intelligent Tutoring Systems (ITS) have proven to be effective
    in addressing the aforementioned challenges by creating personalized learning
    environments that provide instant feedback, adapt to individual student needs,
    and promote the development of procedural knowledge. In the field of automation
    education at the university level, we are creating a 3D artificial intelligence
    (AI)-based ITS software named KIAAA (An AI Assistant for teaching in the field
    of automation), specifically designed to teach computer programming to students.
    KIAAA aims to assist students in transitioning from their abilities to procedural
    aptitude by providing personalized learning scenarios that allow them to apply
    their knowledge and receive immediate feedback. Our approach is based mainly on
    the pedagogical model of ITS, which focuses on creating a supportive and inclusive
    learning environment that promotes success for all students, regardless of their
    initial level of knowledge. One of the key aspects of our approach is the utilization
    of personalized learning. We propose a scheme that, subsequent to evaluate student's
    initial levels of procedural knowledge, creates 3D learning environments tailored
    to each individual student. By analyzing the solutions proposed by the students,
    we select the difficulty level of subsequent tasks. This approach takes into consideration
    student's discrete competence throughout the learning process, enabling them to
    progress on their prior knowledge. Additionally, the software provides customized
    feedback to each student on their performance, helping students identify areas
    that require improvement. Concepts for and implementations of ITS for a variety
    of fields, including introductory programming classes, have evolved for a long
    time. Our main contribution lies in presenting an end to end solution for ITS
    focused on teaching programming for automation students with realistically 3D
    simulated factory environments. While we strongly believe to have created a pedagogically
    sound, integrated intelligent teaching system for assisting programming classes
    in tertiary automation education, a robust user study for methodically evaluating
    our concept and implementation is still to be performed. Thus, we limit ourselves
    to presenting the underlying didactic concepts of KIAAA as a work in progress
    paper with a comprehensive evaluation to follow at a later date.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Asmar
      foaf_name: Ali, Asmar
      foaf_surname: Ali
      foaf_workInfoHomepage: http://www.librecat.org/personId=78685
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Deuter, Andreas
      foaf_surname: Deuter
      foaf_workInfoHomepage: http://www.librecat.org/personId=62088
    orcid: 0000-0002-6529-6215
  - foaf_Person:
      foaf_givenName: Leon
      foaf_name: Wehmeier, Leon
      foaf_surname: Wehmeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=81257
  bibo_doi: 10.1109/fie58773.2023.10343228
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/979-8-3503-3643-6
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: 'Personalized Learning in Automation: A 3D AI-Based Approach@'
...