Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "15832"
  #_source: array:26 [
    "id" => "15832"
    "slug" => "15832-galea-leveraging-generative-agents-in-artifact-evaluation"
    "yearMonth" => "2025-05"
    "year" => "2025"
    "title" => "GALEA – Leveraging Generative Agents in Artifact Evaluation"
    "description" => "PRAT, N., LALOR, J.P. et ABBASI, A. (2025). GALEA – Leveraging Generative Agents in Artifact Evaluation. Dans: <i>Local Solutions for Global Challenges (DESRIST 2025)</i>. Cham:  Springer Nature Switzerland, pp. 83-98."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "PRAT Nicolas"
        "bid" => "B00000434"
        "slug" => "prat-nicolas"
      ]
      1 => array:1 [
        "name" => "Lalor John P."
      ]
      2 => array:1 [
        "name" => "Abbasi Ahmed"
      ]
    ]
    "ouvrage" => "Local Solutions for Global Challenges (DESRIST 2025)"
    "keywords" => array:2 [
      0 => "Large language models"
      1 => "Generative Artificial Intelligence"
    ]
    "updatedAt" => "2025-07-08 11:16:54"
    "publicationUrl" => "https://doi.org/10.1007/978-3-031-93976-1_6"
    "publicationInfo" => array:3 [
      "pages" => "83-98"
      "volume" => ""
      "number" => ""
    ]
    "type" => array:2 [
      "fr" => "Actes d'une conférence"
      "en" => "Conference Proceedings"
    ]
    "support_type" => array:2 [
      "fr" => "Editeur"
      "en" => "Publisher"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "Large language models and generative artificial intelligence are disrupting academic research. They have reached a stage of maturity that enables them to function as proxies for humans in certain tasks and domains. This ability to simulate humans has major implications for the production and evaluation of knowledge. When coupled with agents, large language models become even more powerful proxies for humans. However, several authors have warned against the risks and limitations of using large language models to simulate humans in academic research. In this research, we focus on artifact evaluation leveraging generative agents. We argue that current research lacks a nuanced approach required in the application of generative agents in artifact evaluation, and that the methodological apparatus developed in design science research can provide a basis for this nuanced approach. Building upon this methodological apparatus, we develop a framework for artifact evaluation with generative agents. The main components of the framework are three design principles (applicability depending on artifact and evaluation methods, objectives of evaluation methods with generative agents, implementation choices aligning generative agents with artifact evaluation process), a typology of artifacts, and a matrix of augmentation. We apply the framework to two recent paper exemplars."
      "en" => "Large language models and generative artificial intelligence are disrupting academic research. They have reached a stage of maturity that enables them to function as proxies for humans in certain tasks and domains. This ability to simulate humans has major implications for the production and evaluation of knowledge. When coupled with agents, large language models become even more powerful proxies for humans. However, several authors have warned against the risks and limitations of using large language models to simulate humans in academic research. In this research, we focus on artifact evaluation leveraging generative agents. We argue that current research lacks a nuanced approach required in the application of generative agents in artifact evaluation, and that the methodological apparatus developed in design science research can provide a basis for this nuanced approach. Building upon this methodological apparatus, we develop a framework for artifact evaluation with generative agents. The main components of the framework are three design principles (applicability depending on artifact and evaluation methods, objectives of evaluation methods with generative agents, implementation choices aligning generative agents with artifact evaluation process), a typology of artifacts, and a matrix of augmentation. We apply the framework to two recent paper exemplars."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2025-12-05T13:21:47.000Z"
    "docTitle" => "GALEA – Leveraging Generative Agents in Artifact Evaluation"
    "docSurtitle" => "Conference Proceedings"
    "authorNames" => "<a href="/cv/prat-nicolas">PRAT Nicolas</a>, Lalor John P., Abbasi Ahmed"
    "docDescription" => "<span class="document-property-authors">PRAT Nicolas, Lalor John P., Abbasi Ahmed</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2025</span>"
    "keywordList" => "<a href="#">Large language models</a>, <a href="#">Generative Artificial Intelligence</a>"
    "docPreview" => "<b>GALEA – Leveraging Generative Agents in Artifact Evaluation</b><br><span>2025-05 | Conference Proceedings </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1007/978-3-031-93976-1_6" target="_blank">GALEA – Leveraging Generative Agents in Artifact Evaluation</a>"
  ]
  +lang: "en"
  +"_score": 8.689194
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}