Chat with Smart Conversational Agents

Chen, Xiantao; Mi, Jiaqi; Jia, Menghua; Han, Yajuan; Zhou, Moli; Wu, Tian; Guan, Daisong

doi:10.1145/3338286.3344408

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Nonetheless, a greater alignment in evaluation practices would be desirable in the near future. To this end, we suggest as a further development the creation of a comprehensive evaluation framework, in line with similar research works proposed both for open-domain conversational agents [69,70] and conversational agents for smart-home environments [71], to offer a clearer guidance and support for researchers and developers in the assessment of both feedback and dialogues' success.…”

Section: Discussionmentioning

confidence: 83%

Conversational Agents for Energy Awareness and Efficiency: A Survey

Sanguinetti,

Atzori

2024

Electronics

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The need to reduce greenhouse gas emissions and promote energy efficiency is crucial to achieve the energy transition and sustainable development goals. The availability of tools that provide clear information on energy consumption plays a key role in this transition, enabling users to monitor, manage, and optimize their energy use. This process, commonly referred to as energy feedback or eco-feedback, involves delivering information regarding energy usage and potentially suggesting more sustainable practices. Within the range of available tools, conversational agents can represent a valuable channel to receive detailed information about energy consumption and tailored advice for improving energy efficiency. The aim of this article is thus to explore the application of conversational agents, focusing on eco-feedback, as these tools are primarily devised to foster user awareness of energy usage and enhance more participatory conservation strategies. To this end, we conducted a keyword-based search of major scientific article databases, applying strict criteria to select relevant studies. The results of the collection showed that there is a very diverse landscape with respect to this topic. The surveyed works exhibit a high versatility in feedback goals. Furthermore, while predominantly applied domestically, they also show potential in commercial and industrial settings. Implementation choices also vary to a great extent, while evaluation practices lack a systematic approach and highlight the need for greater consistency. In light of these remarks, we also outline possible future extensions of this type of application, exploring in particular the emerging challenges associated with the increased use of renewable sources and the rise of local decentralized energy communities.

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Section: Discussionmentioning

confidence: 83%

Conversational Agents for Energy Awareness and Efficiency: A Survey

Sanguinetti,

Atzori

2024

Electronics

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“…Expert evaluations have previously been used in relation to MOOCs, for example, to assess the accessibility in Coursera MOOCs (Al-Mouh, Al-Khalifa, & Al-Khalifa, 2014), or to develop a MOOC design model (Gayoung et al, 2016). Expert evaluations have also been used in relation to conversational agents, for example, to design and develop a conversational agent to guide group discussion (Tavanapour, Theodorakopoulos, & Bittner 2020) or to evaluate chat experience with smart conversational agents (Chen et al 2019). In the particular case of the MOOCs, the introduction of complementary external technologies requires a certain maturity level in these to avoid that if they are negatively perceived by learners this could also lead to a negative perception of the MOOC.…”

Section: Methodsmentioning

confidence: 99%

Adaptive learning module for a conversational agent to support MOOC learners

González-Castro

Muñoz‐Merino

Alario-Hoyos

et al. 2021

AJET

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Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions provided to learners considering their level of knowledge using item response theory (IRT) and also provides recommendations of video fragments extracted from the MOOC for when learners fail questions. The adaptive learning module for JavaPAL has been evaluated showing good usability and learnability through the system usability scale (SUS), reasonably suitable video fragments recommendations for learners, and useful visualisations generated as part of the IRT-based adaptation of questions for instructors to better understand what is happening in the course, to design exams, and to redesign the course content.

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Leaders or Team-Mates: Exploring the Role-Based Relationship Between Multiple Intelligent Agents in Driving Scenarios

Xiang

Zhao

et al. 2023

Lecture Notes in Computer Science

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Chat with Smart Conversational Agents

Cited by 8 publications

References 4 publications

Conversational Agents for Energy Awareness and Efficiency: A Survey

Conversational Agents for Energy Awareness and Efficiency: A Survey

Adaptive learning module for a conversational agent to support MOOC learners

Leaders or Team-Mates: Exploring the Role-Based Relationship Between Multiple Intelligent Agents in Driving Scenarios

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