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Deniz Ozturk

Publications and source records attributed to Deniz Ozturk.

3 recordsLinked to original sources

From Instructional Intent to Classroom-Ready Lessons: Investigating Teacher-AI Collaboration in Lesson Authoring for Secondary Computing Education

Generative AI is increasingly studied as a tool for supporting lesson authoring; the creation and adaptation of classroom materials, yet little is known about how it should fit into secondary computing teachers' subject-specific practices. We conducted two complementary qualitative studies with eight secondary computing teachers: focus groups examining existing authoring practices and a think-aloud study using LessonBuilder, a structured AI-supported lesson authoring system, as a research probe. We found that teachers' instructional intentions developed through creating, inspecting, and adapting materials, while teachers selectively delegated tasks to AI and retained responsibility for judging classroom readiness. The think-aloud sessions further showed how examining AI-generated materials prompted teachers to clarify and refine their intentions, making authoring an ongoing negotiation between what teachers intended and what the AI produced. In the context of computing lessons, classroom readiness also involved considering how programming materials and supporting guidance matched students' understanding and intended learning goals. We synthesize these findings into a conceptual model explaining how teachers coordinate instructional intent, AI assistance, and judgments of classroom readiness during lesson authoring. We derive design recommendations for supporting this coordination while preserving teacher control and addressing computing-specific authoring needs.

cs.HC↗

How Children Design and Reason about Trustworthy AI Chatbots

Children increasingly interact with AI chatbots, making trust calibration essential to AI literacy. Prior research has examined children's trust in AI mainly as users evaluating systems built by others, rather than as designers of their own chatbots. We developed a chatbot-building environment with adjustable trust-relevant traits (e.g., confidence, transparency, formality, assertiveness), rules, and persona. We conducted mixed-methods study with 115 learners (ages 8-18) who made 119 chatbots. We examined how children configured their chatbots, reasoned about trustworthiness, and how closely chatbot behavior aligned with their designs. Younger students (age 10-13) set significantly higher confidence than older students (age 14-18), and some deliberately built chatbots that gave wrong answers on purpose, yet still called them trustworthy, arguing that a chatbot does what it was built to do. Younger students equated trust with purpose-fulfillment, while older students linked it to transparent, calibrated design. Students also calibrated academic chatbots to be more transparent and formal than hobby chatbots. We identify seven design dimensions describing what children believe makes a chatbot trustworthy, and discuss implications for AI literacy tools.

cs.HC↗

Will It Teach as Intended? How Teachers Configure Educational AI Chatbots

Teachers are increasingly using generative AI to support instruction, yet it remains unclear how pedagogical intentions are translated into chatbot configurations and reflected in chatbot behavior. We studied a teacher-facing chatbot authoring tool in professional development workshops with 27 middle school teachers, analyzing focus-group interviews alongside configuration and interaction logs. Teachers envisioned chatbots as instructional scaffolds that could provide differentiated support, extend access to assistance, and preserve student thinking within teacher-defined boundaries. Configuration analysis showed that Purpose primarily captured instructional goals and content focus, whereas Rules more often specified pedagogical behavior, guardrails, and learner-specific adaptations. Log-based evaluation showed stronger alignment for responsiveness (88.9%) and persona (81.5%) than for rules (70.4%) and purpose (59.3%). These findings show that configurable controls alone do not ensure pedagogical fidelity and highlight the need for authoring tools that help teachers express, test, and refine intended chatbot behavior.

cs.HC↗