Building trustworthy AI solutions, whether in academia or industry, must take into consideration a number of dimensions including legal, social, ethical, public opinion and environmental aspects. A plethora of guidelines, principles and toolkits have been published globally, but have seen limited grassroots implementation, especially among small and medium sized enterprises (SME), mainly due to lack of knowledge, skills, and resources. In this paper, we report on qualitative SME consultations over two events to establish their understanding of both data and AI ethical principles and to identify the key barriers SMEs face in their adoption of ethical AI approaches. We then use independent experts to review and code 77 published toolkits designed to build and support ethical and responsible AI practices, based on 33 evaluation criteria. The toolkits were evaluated considering their scope to address the identified SME barriers to adoption, human-centric AI principles, AI lifecycle stages, and key themes around responsible AI and practical usability. Toolkits were ranked based on criteria coverage and expert inter-coder agreement. Results show that there is not a one-size-fits-all toolkit that addresses all criteria suitable for SMEs. Our findings show few exemplars of practical application, little guidance on how to use/apply the toolkits and very low uptake by SMEs. Our analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources, and ethical awareness levels focusing initially at the conceptualization stage of the AI lifecycle and then extending throughout.

Comprehension is an important cognitive state for learning. Human tutors recognise comprehension and non-comprehension states by interpreting learner non-verbal behaviour (NVB). Experienced tutors adapt pedagogy, materials and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time assessment for e-learner comprehension of on-screen information could provide a powerful tool for both adaptation within intelligent e-learning platforms and appraisal of tutorial content for learning analytics. However, literature suggests that no existing method for automatic classification of learner comprehension by analysis of NVB can provide a practical solution in an e-learning, on-screen, context. This paper presents design, development and evaluation of COMPASS, a novel near real-time comprehension classification system for use in detecting learner comprehension of on-screen information during e-learning activities. COMPASS uses a novel descriptive analysis of learner behaviour, image processing techniques and artificial neural networks to model and classify authentic comprehension indicative non-verbal behaviour. This paper presents a study in which 44 undergraduate students answered on-screen multiple choice questions relating to computer programming. Using a front-facing USB web camera the behaviour of the learner is recorded during reading and appraisal of on-screen information. The resultant dataset of non-verbal behaviour and question-answer scores has been used to train artificial neural network (ANN) to classify comprehension and non-comprehension states in near real-time. The trained comprehension classifier achieved normalised classification accuracy of 75.8\%.

Student profiling is central to the move from 'one size fits all' computer-aided learning systems to intelligent tutoring systems which adapt to meet the needs of different students. This paper proposes a new method for profiling student learning styles for a conversational intelligent tutoring system (CITS) which utilizes a Mulitlayer Perceptron Artificial Neural Network (MLP-ANN). Throughout an automated conversational tutorial with a CITS, aspects of student behaviour are dynamically captured and input to a Learning Styles Predictor agent to profile an individual's learning style. The proposed method will incorporate a MLP-ANN to combine a set of behaviour traits extracted from the tutoring conversation to improve the accuracy of the learning styles prediction. The paper describes experiments conducted with real students in a live teaching/learning environment for profiling two Felder and Silverman learning styles dimensions. The results show that MLP-ANNs can predict learning styles with an accuracy of 84-89%.