Effects of an artificial agent as a behavioral model on motivational and learning outcomes

Earlier research provides inconsistent evidence for effects of pedagogical agents on learning outcomes. We claim that the agent's instructional method helps to explain the inconsistency, with agents that offer behavioral modeling as an instructional method leading to better learning. We conducted two studies to investigate effects of the use of an artificial agent as a persuasive behavioral model on individuals' learning outcomes in the context of computer training. Specifically, in both studies, participants watched an instructional video on how to perform a web search with their eyes using a novel eye-tracking software. Study 1 examined the effects of agent-delivered modeling vs. two non-modeling instructional methods (agent-delivered instructional narration and no agent, text-only instruction) on participants' (N = 197) self-efficacy and system-specific perceptions of ease of use. Study 2 extends findings of Study 1 by examining effects of agent-delivered modeling vs. two non-modeling instructional methods (agent-delivered instructional narration and no agent, voice-only instructional narration) on participants' (N = 99) declarative knowledge and task performance. Previous work with human behavioral models showed an advantage of behavioral modeling over other non-modeling instructional methods in influencing learning outcomes. Therefore, agent-delivered modeling was predicted to be more effective in influencing motivational (i.e., self-efficacy) and learning outcomes (i.e., declarative knowledge, task performance). In accordance with our hypotheses, results revealed that participants who received instructions from an artificial agent as a behavioral model reported significantly stronger self-efficacy beliefs, tended to have higher system-specific ease of use, exhibited enhanced declarative knowledge, and better task performance skills

The effect of an artificial agent’s vocal expressiveness on immediacy and learning

Background: Though pedagogical artificial agents are expected to play a crucial role in the years to come, earlier studies provide inconsistent results regarding their effect on learning. This might be because their potential for exhibiting subtle nonverbal behaviours we know from human teachers has been untapped. What is more, there is little evidence of the processes underlying the effect of nonverbal behaviours of teachers (either human or artificial) on learning, so as to better guide their practical application. Objectives: The aims of the current research were threefold: firstly, to examine the effect of an artificial agent's vocal expressiveness on non-verbal immediacy (teachers' ability to increase psychological closeness through nonverbal communication). Secondly, to test whether an artificial agent showing strong vocal expressiveness will enhance affective and cognitive learning (perceived and actual), as compared to an artificial agent that shows weak vocal expressiveness. Thirdly, to examine whether the underlying mechanisms of motivation and attention explain the effect of immediacy (and thereby also of vocal expressiveness) on the two learning outcomes. Methods: The study used a between-participants design, with the participants being randomly assigned to one of the two experimental conditions: artificial modelling with strong expressiveness and artificial modelling with weak vocal expressiveness. Results and conclusions: Results showed that an artificial agent with strong vocal expressiveness increased affective and perceived cognitive learning. Partial support was found for actual cognitive learning. What is more, our findings revealed that vocal expressiveness is related to affective and perceived cognitive learning because it promotes nonverbal immediacy. Finally, results provided evidence of motivation as a mediator of the path from immediacy to affective learning. Major takeaways: The current findings verify the important role of nonverbal immediacy found in traditional educational settings. However, showing that these results also apply to artificial teachers is essential, given that the educational landscape is changing and reshaping by artificial intelligence. Thus, taking into consideration the role of vocal expressiveness in the development of artificial teachers or voice assistants like Alexa, Siri, and Google assistant, as a way to enhance immediacy and affective experience of learners is of imperative value, since they will be found more and more in our societies

Persuasive design principles and user models for people with motor disabilities

When developing effective assistive technology, it is crucial to focus on how acceptance and continued use of the technology can be optimized considering the (complexity of the) user and his or her situation. Therefore, this chapter describes methods for creating user models and shows how these were applied to user groups (patients with spinal cord injury, Parkinson's disorder and neuromuscular disorders) of a newly developed assistive technology (AT). The user models include user characteristics such as demographics, relevant medical information, computer interaction behaviour and attitudes towards novel assistive devices. Next, this chapter describes persuasive strategies to improve user acceptance and continued use of AT, specifically aimed at motivating individuals with disabilities to learn to operate the AT and to use it, in order to increase their social participation. Also, this chapter shows how empirical research has tested the effectiveness of the proposed persuasive and personalization (i.e., incorporating user model knowledge) design elements. Finally, this chapter shows how the implications of these findings were used to improve the persuasive design requirements of the AT. In sum, this chapter shows how persuasive personalized design principles (implemented into the AT) improve user acceptance (evaluations) and continued use (performance)

Persuasive design principles and user models for people with motor disabilities

When developing effective assistive technology, it is crucial to focus on how acceptance and continued use of the technology can be optimized considering the (complexity of the) user and his or her situation. Therefore, this chapter describes methods for creating user models and shows how these were applied to user groups (patients with spinal cord injury, Parkinson's disorder and neuromuscular disorders) of a newly developed assistive technology (AT). The user models include user characteristics such as demographics, relevant medical information, computer interaction behaviour and attitudes towards novel assistive devices. Next, this chapter describes persuasive strategies to improve user acceptance and continued use of AT, specifically aimed at motivating individuals with disabilities to learn to operate the AT and to use it, in order to increase their social participation. Also, this chapter shows how empirical research has tested the effectiveness of the proposed persuasive and personalization (i.e., incorporating user model knowledge) design elements. Finally, this chapter shows how the implications of these findings were used to improve the persuasive design requirements of the AT. In sum, this chapter shows how persuasive personalized design principles (implemented into the AT) improve user acceptance (evaluations) and continued use (performance)

Effects of a virtual model’s pitch and speech rate on affective and cognitive learning

Various nonverbal behaviors – often referred to as nonverbal immediacy - of both human and virtual teachers have been shown to play a crucial role in student learning. However, past literature provides limited evidence of the effect of a virtual agent’s vocalization, as a nonverbal immediacy cue, on learning outcomes. Even less is known about the effect of nonverbal immediacy on learning when used in conjunction with virtual behavioral modeling. Earlier research provides evidence that virtual behavioral modeling could be an effective instructional method to facilitate learning in multimedia learning environments. The current work investigated the effects of a virtual model that used stronger vs. weaker vocal nonverbal immediacy on affective learning and cognitive learning (both perceived and objective). The operationalization of the virtual model’s vocal nonverbal immediacy has been realized by manipulating the agent’s vocal parameters of pitch and speech rate. We predicted, that a virtual model with stronger vocal nonverbal immediacy (i.e., higher pitch and faster speech rate) would be more effective in influencing individuals’ learning outcomes, as compared to a virtual model with weaker vocal nonverbal immediacy (i.e., lower pitch and slower speech rate). In accordance with our hypotheses, results revealed that participants who received instructions from a virtual model that used stronger vocal nonverbal immediacy showed greater affective learning, and increased perceptions of learning. Support was also found for an effect on participants’ recall. Results and implications of the study’s findings are discussed

A Multimodal dataset for authoring and editing multimedia content: The MAMEM project

In this report we present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure andcollected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society