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Design and Optimization of an Intelligent Mobile Interaction System for Adult Education.

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Title: Design and Optimization of an Intelligent Mobile Interaction System for Adult Education.
Authors: Chen, Lin1 chen_lin770617@163.com, Han, Linping1 hanlinping66666688@163.com
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 18, p71-85. 15p.
Subjects: Adult education, Mobile learning, Human-computer interaction, Distributed computing, Federated learning, Knowledge transfer, Fuzzy logic
Abstract: Mobile learning scenarios in adult education impose mutually constraining technical requirements regarding real-time interaction performance, recommendation accuracy, and data privacy protection. Neither cloud-centric centralized inference nor device-only local computation can adequately satisfy these demands. In this study, a terminal-edge-cloud collaborative intelligent mobile interaction system was proposed. A fuzzy-inference-based context-awareness engine was deployed on the terminal device, performing local fused inference on temporal, spatial, and device-status information to dynamically generate micro-interaction strategies, with inference latency kept below 5 ms. The recommendation model was compressed to 20 MB through knowledge distillation and 8-bit integer (INT8) quantization, enabling a single inference to be completed within 50 ms. At the edge nodes, user short-term interests were maintained via differentially private gradients, while at the cloud side, model parameters were aggregated through federated averaging to achieve global evolution without raw data leaving the devices. This study provides a feasible technical pathway for the design of mobile interaction systems in resource-constrained and privacy-sensitive scenarios. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Design and Optimization of an Intelligent Mobile Interaction System for Adult Education.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Lin%22">Chen, Lin</searchLink><relatesTo>1</relatesTo><i> chen_lin770617@163.com</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Linping%22">Han, Linping</searchLink><relatesTo>1</relatesTo><i> hanlinping66666688@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 18, p71-85. 15p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Adult+education%22">Adult education</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Mobile learning scenarios in adult education impose mutually constraining technical requirements regarding real-time interaction performance, recommendation accuracy, and data privacy protection. Neither cloud-centric centralized inference nor device-only local computation can adequately satisfy these demands. In this study, a terminal-edge-cloud collaborative intelligent mobile interaction system was proposed. A fuzzy-inference-based context-awareness engine was deployed on the terminal device, performing local fused inference on temporal, spatial, and device-status information to dynamically generate micro-interaction strategies, with inference latency kept below 5 ms. The recommendation model was compressed to 20 MB through knowledge distillation and 8-bit integer (INT8) quantization, enabling a single inference to be completed within 50 ms. At the edge nodes, user short-term interests were maintained via differentially private gradients, while at the cloud side, model parameters were aggregated through federated averaging to achieve global evolution without raw data leaving the devices. This study provides a feasible technical pathway for the design of mobile interaction systems in resource-constrained and privacy-sensitive scenarios. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=197602526
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        Value: 10.3991/ijim.v20i18.63064
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        Text: English
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        PageCount: 15
        StartPage: 71
    Subjects:
      – SubjectFull: Adult education
        Type: general
      – SubjectFull: Mobile learning
        Type: general
      – SubjectFull: Human-computer interaction
        Type: general
      – SubjectFull: Distributed computing
        Type: general
      – SubjectFull: Federated learning
        Type: general
      – SubjectFull: Knowledge transfer
        Type: general
      – SubjectFull: Fuzzy logic
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              M: 09
              Text: 2026
              Type: published
              Y: 2026
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